{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to 1D Convolutional Neural Networks for NLP\n",
    "This is the code corresponding to the CNN part of my notes [Introduction to CNNs and LSTMs for NLP](http://www.lix.polytechnique.fr/~anti5662/intro_cnn_lstm_tixier.pdf). I encourage you to have a look at these notes for a quick theoretical intro.\n",
    "\n",
    "In this notebook, we use Python 3 and [Keras](https://keras.io/getting-started/faq/}) with `TensorFlow` backend to perform binary **movie review classification** (positive/negative) on the IMDB dataset (25K training, 25K testing) with the basic CNN architecture described in [CNNs for Sentence Classification (Kim, EMNLP 2014)](https://arxiv.org/abs/1408.5882). We also visualize **document embeddings**, **predictive regions** in the input documents, following [Effective Use of Word Order for Text Categorization with Convolutional Neural Networks (Johnson and Zhang, NAACL 2015)](https://arxiv.org/pdf/1412.1058.pdf), and first-order derivate **saliency maps**, following [Visualizing and Understanding Neural Models in NLP (Li et al. 2015)](https://arxiv.org/abs/1506.01066). \n",
    "\n",
    "\n",
    "## Environment setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "packages loaded\n",
      "functions defined\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import csv\n",
    "import json\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "from gensim.models.word2vec import Word2Vec\n",
    "\n",
    "from keras.models import Model, load_model\n",
    "from keras import backend as K\n",
    "from keras.layers import Input, Embedding, Dropout, Conv1D, GlobalMaxPooling1D, Dense, Concatenate\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "\n",
    "print('packages loaded')\n",
    "\n",
    "def extract_regions(tokens, filter_size):\n",
    "    regions = []\n",
    "    regions.append(' '.join(tokens[:filter_size]))\n",
    "    for i in range(filter_size, len(tokens)):\n",
    "        regions.append(' '.join(tokens[(i-filter_size+1):(i+1)]))\n",
    "    return regions\n",
    "\n",
    "print('functions defined')\n",
    "\n",
    "path_root = # fill me \n",
    "path_to_IMDB = path_root + 'new_data\\\\'\n",
    "path_to_pretrained_wv = # fill me\n",
    "path_to_plot = path_root\n",
    "path_to_save = path_root"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameter values\n",
    "\n",
    "Let's set the values of our parameters. Below are some explanations about the role of some parameters. The others are pretty self-explanatory.\n",
    "* `use_pretrained`: whether to use pre-trained word embeddings or not (convergence is faster, and absolute accuracy is slightly greater)\n",
    "* `max_features`: size of our vocab\n",
    "* `stpwd_thd`: number of most frequent words to consider as stopwords\n",
    "* `max_size`: maximum document size allowed\n",
    "* `nb_epoch`: increasing the number of epochs may lead to overfitting when max_size is small (especially since dataset is small in the first place)\n",
    "* `my_optimizer`: `adam` proved better than `SGD` and `Adadelta` in my experiments\n",
    "* `my_patience`: for early stopping strategy (number of epochs without improvement to wait before we stop training)\n",
    "\n",
    "Note that since we train on CPU, we select affordable parameter values. On GPU, we would be able to afford bigger vocab, longer maximum document size, more branches, and more filters per branch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best model will be saved with name: my_cnn_two_branches.hdf5\n"
     ]
    }
   ],
   "source": [
    "use_pretrained = True\n",
    "name_save = 'my_cnn_two_branches.hdf5'\n",
    "print('best model will be saved with name:',name_save)\n",
    "\n",
    "max_features = int(2e4)\n",
    "stpwd_thd = 1 \n",
    "max_size = 200\n",
    "word_vector_dim = int(3e2)\n",
    "do_static = False\n",
    "nb_filters = 150\n",
    "filter_size_a = 3\n",
    "filter_size_b = 4\n",
    "drop_rate = 0.3\n",
    "batch_size = 64\n",
    "nb_epoch = 6\n",
    "my_optimizer = 'adam' \n",
    "my_patience = 2\n",
    "\n",
    "if not use_pretrained:\n",
    "    # if the embeddings are initialized randomly, using static mode doesn't make sense\n",
    "    do_static = False\n",
    "    print(\"not using pre-trained embeddings, overwriting 'do_static' argument\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data loading and further preprocessing\n",
    "### Reading and inspection\n",
    "Let's read the data. The raw data have already been pre-processed by me in this [script](https://github.com/Tixierae/deep_learning_NLP/blob/master/CNN_IMDB/imdb_preprocess_new.py). `word_to_index` is a dictionary of word indexes sorted by decreasing frequency across the corpus. It is a 1-based index, as 0 is reserved for zero-padding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index of \"the\": 1\n",
      "index of \"movie\": 18\n",
      "index of \"!\": 33\n",
      "index of \":)\": 2612\n",
      "index of \"elephant\": 8150\n",
      "index of \"dreaded\": 12478\n"
     ]
    }
   ],
   "source": [
    "with open(path_to_IMDB + 'word_to_index.json', 'r') as my_file:\n",
    "    word_to_index = json.load(my_file)\n",
    "\n",
    "# examples:\n",
    "print('index of \"the\":',word_to_index['the']) # most frequent word\n",
    "print('index of \"movie\":',word_to_index['movie']) # very frequent word\n",
    "print('index of \"!\":',word_to_index['!']) # very frequent punctuation mark\n",
    "print('index of \":)\":',word_to_index[':)']) # frequent emoji\n",
    "print('index of \"elephant\":',word_to_index['elephant']) # less frequent word\n",
    "print('index of \"dreaded\":',word_to_index['dreaded']) # very infrequent word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data loaded\n"
     ]
    }
   ],
   "source": [
    "with open(path_to_IMDB + 'training.csv', 'r') as my_file:\n",
    "    reader = csv.reader(my_file, delimiter=',')\n",
    "    x_train = list(reader)\n",
    "\n",
    "with open(path_to_IMDB + 'test.csv', 'r') as my_file:\n",
    "    reader = csv.reader(my_file, delimiter=',')\n",
    "    x_test = list(reader)\n",
    "\n",
    "with open(path_to_IMDB + 'training_labels.txt', 'r') as my_file:\n",
    "    y_train = my_file.read().splitlines()\n",
    "\n",
    "with open(path_to_IMDB + 'test_labels.txt', 'r') as my_file:\n",
    "    y_test = my_file.read().splitlines()\n",
    "\n",
    "# turn lists of strings into lists of integers\n",
    "x_train = [[int(elt) for elt in sublist] for sublist in x_train]\n",
    "x_test = [[int(elt) for elt in sublist] for sublist in x_test]  \n",
    "\n",
    "y_train = [int(elt) for elt in y_train]\n",
    "y_test = [int(elt) for elt in y_test]\n",
    "\n",
    "print('data loaded')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can verify what our data look like by inverting the `word_to_index` dictionary and comparing a random review to the real one: [https://www.imdb.com/review/rw1958804/?ref_=tt_urv](https://www.imdb.com/review/rw1958804/?ref_=tt_urv):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Although i had heard this film was a little dry , I watch whatever Scott Bakula is in . At the start of this film I had high hopes for the classic cheesy but enjoyable Scott-gets-girl ending and until 20 minutes before the end it was going great . The plot twist was crazy and unexpected and very clever . I kept my fingers crossed that it would work out and it would all be some horrible misunderstanding , right up until when the credits rolled and I realised that there was not going to be a happy and contented ending . Unfortunately i was left regretting that i'd watched it and hurriedly putting on some quantum leap to restore my faith in the goodness of the Great Scott !\n"
     ]
    }
   ],
   "source": [
    "index_to_word = dict((v,k) for k, v in word_to_index.items())\n",
    "print (' '.join([index_to_word[elt] for elt in x_train[4]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stopwords and out-of-vocab words removal\n",
    "We only take into account the `max_features` most frequent words and disregard the `stopword_threhsold` most frequent words. Here, we don't remove any stopwords as some of them might be useful for classification ('but','not', etc.) and the model is supposed to be pretty robust to the presence of unpredictive features, as it automatically learns which features are the most discriminative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stopwords are: []\n",
      "pruning done\n"
     ]
    }
   ],
   "source": [
    "stpwds = [index_to_word[idx] for idx in range(1,stpwd_thd)]\n",
    "print('stopwords are:',stpwds)\n",
    "\n",
    "x_train = [[elt for elt in rev if elt<=max_features and elt>=stpwd_thd] for rev in x_train]\n",
    "x_test =  [[elt for elt in rev if elt<=max_features and elt>=stpwd_thd] for rev in x_test]\n",
    "\n",
    "print('pruning done')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Truncation and zero-padding\n",
    "We truncate reviews of size larger than `max_size` to their `max_size` first words. We also pad reviews shorter than `max_size` with the zero vector reserved for padding (0th index):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "padding 13461 reviews from the training set\n",
      "padding 13708 reviews from the test set\n",
      "truncation and padding done\n"
     ]
    }
   ],
   "source": [
    "x_train = [rev[:max_size] for rev in x_train]\n",
    "x_test = [rev[:max_size] for rev in x_test]\n",
    "\n",
    "print('padding',len([elt for elt in x_train if len(elt)<max_size])\n",
    ",'reviews from the training set')\n",
    "\n",
    "x_train = [rev+[0]*(max_size-len(rev)) if len(rev)<max_size else rev for rev in x_train]\n",
    "\n",
    "# sanity check: all reviews should now be of size 'max_size'\n",
    "assert(max_size == list(set([len(rev) for rev in x_train]))[0]),\"1st sanity check failed!\"\n",
    "\n",
    "print('padding',len([elt for elt in x_test if len(elt)<max_size]),'reviews from the test set')\n",
    "\n",
    "x_test = [rev+[0]*(max_size-len(rev)) if len(rev)<max_size else rev for rev in x_test]\n",
    "\n",
    "# sanity check\n",
    "assert(max_size == list(set([len(rev) for rev in x_test]))[0]),\"2nd sanity check failed!\"\n",
    "\n",
    "print('truncation and padding done')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading pre-trained word vectors\n",
    "\n",
    "300-dimensional word embeddings learned with word2vec on the GoogleNews dataset can be downloaded from https://code.google.com/archive/p/word2vec/Google, under the section \"Pre-trained word and phrase vectors\". Using `gensim`, we can load only the vectors of the words found in our vocabulary and save some RAM. This takes about 90s on my laptop.\n",
    "\n",
    "* Note 1: when using the `intersect_word2vec_format` method, in-vocab words without an entry in the binary file are not removed from the vocabulary. Instead, their vectors are silently initialized to random values. We can detect those vectors via their norms which approach zero. This is what we do in lines `25-28`. Most of those words are proper nouns, which don't have an entry in the word vectors file because we lowercased the text. Those words will start as random and be tuned during training.\n",
    "\n",
    "* Note 2: lines `31-35`, we create and fill an embedding table of size `max_features + 1`, ignoring stopword removal and truncation. E.g., if `max_features = 2e4`, reviews will be composed of integers from `1` to `2e4` (plus `0` after padding). But after truncation and stopword removal, the vocabulary size (number of unique integer values) may become smaller. So, if the input dim is based on the final vocab size, some integers in the range `[1,2e4]` won't have a row anymore in the embedding table. This is why we create the embedding lookup table based on the original `max_features +1` value, knowing that the rows of the words that have been removed just won't be looked up.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8224882 words\n",
      "20000 unique words\n",
      "pre-trained word vectors loaded\n",
      "# of vocab words w/o a Google News entry: 1139\n",
      "embeddings created\n"
     ]
    }
   ],
   "source": [
    "# convert integer reviews into word reviews\n",
    "x_full = x_train + x_test\n",
    "x_full_words = [[index_to_word[idx] for idx in rev if idx!=0] for rev in x_full]\n",
    "all_words = [word for rev in x_full_words for word in rev]\n",
    "\n",
    "print(len(all_words),'words')\n",
    "print(len(list(set(all_words))),'unique words')\n",
    "\n",
    "if use_pretrained:\n",
    "\n",
    "    # initialize word vectors\n",
    "    word_vectors = Word2Vec(size=word_vector_dim, min_count=1)\n",
    "\n",
    "    # create entries for the words in our vocabulary\n",
    "    word_vectors.build_vocab(x_full_words)\n",
    "\n",
    "    # sanity check\n",
    "    assert(len(list(set(all_words))) == len(word_vectors.wv.vocab)),\"3rd sanity check failed!\"\n",
    "\n",
    "    # fill entries with the pre-trained word vectors\n",
    "    word_vectors.intersect_word2vec_format(path_to_pretrained_wv + 'GoogleNews-vectors-negative300.bin.gz', binary=True)\n",
    "\n",
    "    print('pre-trained word vectors loaded')\n",
    "\n",
    "    norms = [np.linalg.norm(word_vectors[word]) for word in list(word_vectors.wv.vocab)] # in Python 2.7: word_vectors.wv.vocab.keys()\n",
    "    idxs_zero_norms = [idx for idx, norm in enumerate(norms) if norm<0.05]\n",
    "    no_entry_words = [list(word_vectors.wv.vocab)[idx] for idx in idxs_zero_norms]\n",
    "    print('# of vocab words w/o a Google News entry:',len(no_entry_words))\n",
    "\n",
    "    # create numpy array of embeddings  \n",
    "    embeddings = np.zeros((max_features + 1,word_vector_dim))\n",
    "    for word in list(word_vectors.wv.vocab):\n",
    "        idx = word_to_index[word]\n",
    "        # word_to_index is 1-based! the 0-th row, used for padding, stays at zero\n",
    "        embeddings[idx,] = word_vectors[word]\n",
    "        \n",
    "    print('embeddings created')\n",
    "\n",
    "else:\n",
    "    print('not using pre-trained embeddings')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining CNN\n",
    "For efficiency reasons, we will only implement two branches of the following architecture: \n",
    "\n",
    "<img src=\"https://github.com/Tixierae/deep_learning_NLP/raw/master/CNN_IMDB/cnn_illustration.png\" alt=\"Drawing\" style=\"width: 400px;\"/>\n",
    "\n",
    "By branch, I mean the part of the architecture that corresponds to a given filter size (e.g., the upper red part is one branch)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "branch A: 150 feature maps of size 198\n",
      "branch B: 150 feature maps of size 197\n",
      "model compiled\n"
     ]
    }
   ],
   "source": [
    "my_input = Input(shape=(max_size,)) # we leave the 2nd argument of shape blank because the Embedding layer cannot accept an input_shape argument\n",
    "\n",
    "if use_pretrained:\n",
    "    embedding = Embedding(input_dim=embeddings.shape[0], # vocab size, including the 0-th word used for padding\n",
    "                          output_dim=word_vector_dim,\n",
    "                          weights=[embeddings], # we pass our pre-trained embeddings\n",
    "                          input_length=max_size,\n",
    "                          trainable=not do_static,\n",
    "                          ) (my_input)\n",
    "else:\n",
    "    embedding = Embedding(input_dim=max_features + 1,\n",
    "                          output_dim=word_vector_dim,\n",
    "                          trainable=not do_static,\n",
    "                          ) (my_input)\n",
    "\n",
    "embedding_dropped = Dropout(drop_rate)(embedding)\n",
    "\n",
    "# feature map size should be equal to max_size-filter_size+1\n",
    "# tensor shape after conv layer should be (feature map size, nb_filters)\n",
    "print('branch A:',nb_filters,'feature maps of size',max_size-filter_size_a+1)\n",
    "print('branch B:',nb_filters,'feature maps of size',max_size-filter_size_b+1)\n",
    "\n",
    "# A branch\n",
    "conv_a = Conv1D(filters = nb_filters,\n",
    "              kernel_size = filter_size_a,\n",
    "              activation = 'relu',\n",
    "              )(embedding_dropped)\n",
    "\n",
    "pooled_conv_a = GlobalMaxPooling1D()(conv_a)\n",
    "\n",
    "pooled_conv_dropped_a = Dropout(drop_rate)(pooled_conv_a)\n",
    "\n",
    "# B branch\n",
    "conv_b = Conv1D(filters = nb_filters,\n",
    "              kernel_size = filter_size_b,\n",
    "              activation = 'relu',\n",
    "              )(embedding_dropped)\n",
    "\n",
    "pooled_conv_b = GlobalMaxPooling1D()(conv_b)\n",
    "\n",
    "pooled_conv_dropped_b = Dropout(drop_rate)(pooled_conv_b)\n",
    "\n",
    "concat = Concatenate()([pooled_conv_dropped_a,pooled_conv_dropped_b])\n",
    "\n",
    "concat_dropped = Dropout(drop_rate)(concat)\n",
    "\n",
    "# we finally project onto a single unit output layer with sigmoid activation\n",
    "prob = Dense(units = 1, # dimensionality of the output space\n",
    "             activation = 'sigmoid',\n",
    "             )(concat_dropped)\n",
    "\n",
    "model = Model(my_input, prob)\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer = my_optimizer,\n",
    "              metrics = ['accuracy'])\n",
    "\n",
    "print('model compiled')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can print a summary of the model with `model.summary()`, and access the individual layers of the model with `model.layers`, then, the input/output shape of each layer with, e.g., `model.layers[0].input_shape` or `model.layers[0].output_shape`. For instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 200)          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_1 (Embedding)         (None, 200, 300)     6000300     input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 200, 300)     0           embedding_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1 (Conv1D)               (None, 198, 150)     135150      dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_2 (Conv1D)               (None, 197, 150)     180150      dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_1 (GlobalM (None, 150)          0           conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_2 (GlobalM (None, 150)          0           conv1d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 150)          0           global_max_pooling1d_1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 150)          0           global_max_pooling1d_2[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 300)          0           dropout_2[0][0]                  \n",
      "                                                                 dropout_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_4 (Dropout)             (None, 300)          0           concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 1)            301         dropout_4[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 6,315,901\n",
      "Trainable params: 6,315,901\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, 197, 150)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()\n",
    "model.layers[4].output_shape # dimensionality of document encodings (nb_filters*2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of parameters of the model\n",
    "* embedding layer: the number of entries in the embedding layer (`max_features` x `word_vector_dim`)\n",
    "* conv layer: `filter_size` x filter depth (`word_vector_dim`) x `nb_filters` + `nb_filters` (one bias for each filter)\n",
    "* dense layer: input size x output size + one bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total number of model parameters: 6315901\n"
     ]
    }
   ],
   "source": [
    "print('total number of model parameters:',model.count_params())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization of document embeddings before training\n",
    "We extract the output of the final embedding layer (before the softmax), which gives the encoding of the input document for some documents (`n_plot`) of the test set. We then visualize a low-dimensional map of the embeddings. We can see that before training, the documents are dispersed randomly in the space (which makes sense)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plotting embeddings of first 1000 documents\n"
     ]
    },
    {
     "data": {
      "image/png": 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VNrC1t2gqemVAP+cPZ6hugjeu1UjZY9+Ri1fzejbD01zCoWU6YxatbQGbsK7C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\n0TQJWuFrNBpNk6AVvkaj0TQJ/x8WjPQV/IgdkQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x42ab2cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# in test mode, we should set the 'learning_phase' flag to 0 (we don't want to use dropout)\n",
    "get_doc_embedding = K.function([model.layers[0].input,K.learning_phase()],\n",
    "                               [model.layers[9].output])\n",
    "\n",
    "n_plot = 1000\n",
    "print('plotting embeddings of first',n_plot,'documents')\n",
    "\n",
    "doc_emb = get_doc_embedding([np.array(x_test[:n_plot]),0])[0]\n",
    "\n",
    "my_pca = PCA(n_components=10)\n",
    "my_tsne = TSNE(n_components=2,perplexity=10) #https://lvdmaaten.github.io/tsne/\n",
    "doc_emb_pca = my_pca.fit_transform(doc_emb) \n",
    "doc_emb_tsne = my_tsne.fit_transform(doc_emb_pca)\n",
    "\n",
    "labels_plt = y_test[:n_plot]\n",
    "my_colors = ['blue','red']\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for label in list(set(labels_plt)):\n",
    "    idxs = [idx for idx,elt in enumerate(labels_plt) if elt==label]\n",
    "    ax.scatter(doc_emb_tsne[idxs,0], \n",
    "               doc_emb_tsne[idxs,1], \n",
    "               c = my_colors[label],\n",
    "               label=str(label),\n",
    "               alpha=0.7,\n",
    "               s=10)\n",
    "\n",
    "ax.legend(scatterpoints=1)\n",
    "fig.suptitle('t-SNE visualization of CNN-based doc embeddings \\n (first 1000 docs from test set)',fontsize=10)\n",
    "fig.set_size_inches(6,4)\n",
    "#fig.savefig(path_to_plot + 'doc_embeddings_init.pdf',bbox_inches='tight')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training CNN\n",
    "We train the model on CPU (warning: by default, will use all the cores of the machine!). Note you can get a significant speedup by using a GPU. We also add two callbacks:\n",
    "* the first one ensures that training stops after `my_patience` epochs without improvement in test set accuracy (early stopping strategy)\n",
    "* the second one (checkpointer) saves the model to disk for every epoch for which there is improvement. Therefore, at the end of training, the model saved on disk will be the one corresponding to the best epoch and we can reload it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/6\n",
      "25000/25000 [==============================] - 623s 25ms/step - loss: 0.4647 - acc: 0.7711 - val_loss: 0.3315 - val_acc: 0.8540\n",
      "Epoch 2/6\n",
      "25000/25000 [==============================] - 633s 25ms/step - loss: 0.2892 - acc: 0.8801 - val_loss: 0.3006 - val_acc: 0.8702\n",
      "Epoch 3/6\n",
      "25000/25000 [==============================] - 612s 24ms/step - loss: 0.2041 - acc: 0.9202 - val_loss: 0.3166 - val_acc: 0.8675\n",
      "Epoch 4/6\n",
      "25000/25000 [==============================] - 544s 22ms/step - loss: 0.1376 - acc: 0.9495 - val_loss: 0.3542 - val_acc: 0.8620\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x212e3c50>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "early_stopping = EarlyStopping(monitor='val_acc', # go through epochs as long as accuracy on validation set increases\n",
    "                               patience=my_patience,\n",
    "                               mode='max')\n",
    "\n",
    "# make sure that the model corresponding to the best epoch is saved\n",
    "checkpointer = ModelCheckpoint(filepath=path_to_save + name_save,\n",
    "                               monitor='val_acc',\n",
    "                               save_best_only=True,\n",
    "                               verbose=0)\n",
    "\n",
    "model.fit(x_train, \n",
    "          y_train,\n",
    "          batch_size = batch_size,\n",
    "          epochs = nb_epoch,\n",
    "          validation_data = (x_test, y_test),\n",
    "          callbacks = [early_stopping,checkpointer])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to make sure we load the model corresponding to the best epoch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = load_model(path_to_save + name_save)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing and understanding CNN\n",
    "\n",
    "### Document embeddings after training\n",
    "We can see that after only a few epochs, our model has already learned meaningful internal representations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plotting embeddings of first 1000 documents\n"
     ]
    },
    {
     "data": {
      "image/png": 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qDLs6pM1MWaop6QnLkPj1Y9hF1ksTbHi4qPM/Y4aMydZWWYidbV9fL+PWWFSb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\n4QuCIDQJ/w9YvD5rE5YSWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x45bc8dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('plotting embeddings of first',n_plot,'documents')\n",
    "\n",
    "doc_emb = get_doc_embedding([np.array(x_test[:n_plot]),0])[0]\n",
    "\n",
    "my_pca = PCA(n_components=10)\n",
    "my_tsne = TSNE(n_components=2,perplexity=10)\n",
    "doc_emb_pca = my_pca.fit_transform(doc_emb) \n",
    "doc_emb_tsne = my_tsne.fit_transform(doc_emb_pca)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for label in list(set(labels_plt)):\n",
    "    idxs = [idx for idx,elt in enumerate(labels_plt) if elt==label]\n",
    "    ax.scatter(doc_emb_tsne[idxs,0], \n",
    "               doc_emb_tsne[idxs,1], \n",
    "               c = my_colors[label],\n",
    "               label=str(label),\n",
    "               alpha=0.7,\n",
    "               s=10)\n",
    "\n",
    "ax.legend(scatterpoints=1)\n",
    "fig.suptitle('t-SNE visualization of CNN-based doc embeddings \\n (first 1000 docs from test set)',fontsize=10)\n",
    "fig.set_size_inches(6,4)\n",
    "#fig.savefig(path_to_plot + 'doc_embeddings.pdf',bbox_inches='tight')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictive text regions\n",
    "Here we follow the approach of [Effective Use of Word Order for Text Categorization with Convolutional Neural Networks (Johnson and Zhang, NAACL 2015)](https://arxiv.org/pdf/1412.1058.pdf) (see Tables 5 and 6).\n",
    "\n",
    "The feature maps that we find at the output of the convolutional layer provide region embeddings (in an `nb_filters`-dimensional space). For a given branch associated with `filter_size`, there are `max_size-filter_size+1` regions of size `filter_size` for an input of size `max_size`. For a given document, we want to identify the `n_show` regions of each branch that are associated with the highest weights in the corresponding feature maps. \n",
    "\n",
    "We can see that to classify the test set documents (which the model has never seen), the CNN uses regions of the input documents that make sense to us as humans. It picks up the compliments (`\"I highly recommend\"`, `\"also very funny\"`) and critics (`\"were so poor\"`, `\"No screenplay , no\"`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " *********\n",
      "===== text: =====\n",
      "My original title for this review was going to be , \" Ending disappoints , Film triumphs . \" But I actually thought about this one on the way home . It is not the fairy tale most of these films are , it takes turns that are different and while its ending is at first disappointing , it slowly sinks in and hits the core on a satisfying plot follows a man named James Aaron ( Your Jeff , who also wrote and directed ) a struggling actor who lives in Chicago with his mother and deals with both his and his inability to find someone to , it sounds corny , but it experiments with elements that make it somewhat unpredictable , and actually makes you wish it were longer . The ending came kind of abruptly and had me saying , \" that's it ! ? \" But once it starts to take it's toll , it really makes you does have many tones of seriousness throughout , but fear not , for it is also very funny . Some scenes offer huge laughs , and those who have seen Jeff stand-up will recognize a couple (\n",
      "===== label: 1 =====\n",
      "===== prediction: [ 0.76169044] =====\n",
      "===== most predictive regions of size 3 : =====\n",
      "['at first disappointing', 'also very funny', 'very funny .']\n",
      "===== most predictive regions of size 4 : =====\n",
      "['first disappointing , it', 'core on a satisfying', 'is also very funny']\n",
      "\n",
      " *********\n",
      "===== text: =====\n",
      "\" Death Promise \" is a lost 70 ' s exploitation gem and deserves to be seen . Technically somewhat of a mess and boasting a stock of amateur New types , this film never bores . I highly recommend tracking this down . It's a hoot and a half .\n",
      "===== label: 1 =====\n",
      "===== prediction: [ 0.77248842] =====\n",
      "===== most predictive regions of size 3 : =====\n",
      "['gem and deserves', 'I highly recommend', 'highly recommend tracking']\n",
      "===== most predictive regions of size 4 : =====\n",
      "['of a mess and', 'I highly recommend tracking', 'highly recommend tracking this']\n",
      "\n",
      " *********\n",
      "===== text: =====\n",
      "Having endured this film last night , I turned off the DVD player with a sense of deserving a medal for having the to see it through to the end . Throughout the film I felt that I was watching the storyline that you get in a high budget porn movie . the acting was stiff and taut , camera work appalling , and the locations and sets were so poor it felt like they had borrowed them from the local High School \" Society \" . The only saving grace for this movie was that it had Amy Adams and Harriet Harris in its credits , other than that it was pure dribble .\n",
      "===== label: 0 =====\n",
      "===== prediction: [ 0.06151607] =====\n",
      "===== most predictive regions of size 3 : =====\n",
      "['were so poor', 'so poor it', 'saving grace for']\n",
      "===== most predictive regions of size 4 : =====\n",
      "['were so poor it', 'so poor it felt', 'only saving grace for']\n",
      "\n",
      " *********\n",
      "===== text: =====\n",
      "The movie is a happy , was made to make us sleep . And that ´ s what we do , as we dream about the top beautiful Natasha . No screenplay , no deep characters , nothing special . So , let ´ s sleep .\n",
      "===== label: 0 =====\n",
      "===== prediction: [ 0.47418728] =====\n",
      "===== most predictive regions of size 3 : =====\n",
      "['the top beautiful', 'top beautiful Natasha', '. No screenplay']\n",
      "===== most predictive regions of size 4 : =====\n",
      "['No screenplay , no', 'deep characters , nothing', 'nothing special . So']\n"
     ]
    }
   ],
   "source": [
    "get_region_embedding_a = K.function([model.layers[0].input,K.learning_phase()],\n",
    "                                    [model.layers[3].output])\n",
    "\n",
    "get_region_embedding_b = K.function([model.layers[0].input,K.learning_phase()],\n",
    "                                    [model.layers[4].output])\n",
    "\n",
    "get_softmax = K.function([model.layers[0].input,K.learning_phase()],\n",
    "                         [model.layers[11].output])\n",
    "\n",
    "n_doc_per_label = 2\n",
    "idx_pos = [idx for idx,elt in enumerate(y_test) if elt==1]\n",
    "idx_neg = [idx for idx,elt in enumerate(y_test) if elt==0]\n",
    "my_idxs = idx_pos[:n_doc_per_label] + idx_neg[:n_doc_per_label]\n",
    "\n",
    "x_test_my_idxs = np.array([x_test[elt] for elt in my_idxs])\n",
    "y_test_my_idxs = [y_test[elt] for elt in my_idxs]\n",
    "\n",
    "reg_emb_a = get_region_embedding_a([x_test_my_idxs,0])[0]\n",
    "reg_emb_b = get_region_embedding_b([x_test_my_idxs,0])[0]\n",
    "\n",
    "# predictions are probabilities of belonging to class 1\n",
    "predictions = get_softmax([x_test_my_idxs,0])[0] \n",
    "# note: you can also use directly: predictions = model.predict(x_test[:100]).tolist()\n",
    "\n",
    "n_show = 3 # number of most predictive regions we want to display\n",
    "\n",
    "for idx,doc in enumerate(x_test_my_idxs):\n",
    "        \n",
    "    tokens = [index_to_word[elt] for elt in doc if elt!=0] # the 0 index is for padding\n",
    "    \n",
    "    # extract regions (sliding window over text)\n",
    "    regions_a = extract_regions(tokens, filter_size_a)\n",
    "    regions_b = extract_regions(tokens, filter_size_b)\n",
    "    \n",
    "    print('\\n *********')\n",
    "    print('===== text: =====')\n",
    "    print(' '.join(tokens))\n",
    "    print('===== label:',y_test_my_idxs[idx],'=====')\n",
    "    print('===== prediction:',predictions[idx],'=====')\n",
    "    norms_a = np.linalg.norm(reg_emb_a[idx,:,:],axis=1)\n",
    "    norms_b = np.linalg.norm(reg_emb_b[idx,:,:],axis=1)\n",
    "    print('===== most predictive regions of size',filter_size_a,': =====')\n",
    "    print([elt for idxx,elt in enumerate(regions_a) if idxx in np.argsort(norms_a)[-n_show:]]) # 'np.argsort' sorts by increasing order\n",
    "    print('===== most predictive regions of size',filter_size_b,': =====')\n",
    "    print([elt for idxx,elt in enumerate(regions_b) if idxx in np.argsort(norms_b)[-n_show:]])\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saliency maps\n",
    "Here we follow one of the approaches proposed in [Visualizing and Understanding Neural Models in NLP (Li et al. 2015)](https://arxiv.org/abs/1506.01066).\n",
    "\n",
    "The idea is to rank the elements of the input document based on their influence on the prediction. An approximation can be given by the magnitudes of the first-order partial derivatives of the output of the model with respect to each word in the input document. The interpretation is that we identify which words in the document need to be *changed the least to change the class score the most*. The derivatives can be obtained by performing a single back-propagation pass. Note that here, we backpropagate the **class score** and not the loss (like we do during training).\n",
    "\n",
    "You can view the saliency maps as vector graphics PDFs here: https://github.com/Tixierae/deep_learning_NLP/tree/master/CNN_IMDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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erQPX+NPPOZfxhWc5ly1cVHhmnpI4n/ZupHN3Ojc53HgLSQ3pAYWr4B7Uaz5J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\nrd3KrQD4Gt1GllQ0ha/vYBFOvqqngxU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Twog4AY5L7XH4VK8QuhTmhcIjuvRt3qPwFk6y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oljznuNe85ytGoTnZT4U+49tBpeSv5wQBrbUdguvPmK116uiLS3Nlod3APNTSKtGd5s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\ndRNtEPuZzcmr5oY2cOdQs+QS87HzRo9s4rSVbNMYjbFUZjWY0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m5+b7DcqaY32nY1K0tP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qNiVS0uJRcBfQSoMHLSzAgaVl4E8I9hc4dAJaxVGrS1rtHzR2Q9LCIGC2/LHK1TNZQ6VaSqqg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2608d080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CCCGEEEJMHq7uPpMwLW8Sgu0wb/5ramzI7ENJ9ScNgLGxmA+Aiuy7NBcODoffLU1RXRlY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\nqJnEsQdSSt+X9FK1eFp73CnpS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      "text/plain": [
       "<matplotlib.figure.Figure at 0x267b5dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Tpy9HzTTnKPXDHROzkr+o6pm4izbbwxgJk3+BbkcfmI3jwHnE/uX7gnNtoP2o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rjzUveGEF4VQrg/hPDJEMKDIYTXXGoc/XwR/44QwhtCCG+S9EpJe+wFIYR78sAePHH8eB+7djgc\nDofD4XA4XliEEAYlvVvSWyTdIenbQwh3mMveIunW/O8eSb90Gff+nKSfjDG+StK/yJ8vin6+5D8i\naaek2/Ogh+0FNSd/46ZNfeza4XA4HA6Hw+FIL7fL+e8SeI2kx2KMT8QYZyT9rqS3mmveKum3YsL9\nktaFELZf4t4oaU3+fa2kZy41kH5y8t9zJdfPL0SduTBbuLnwwOD97s15YOH61vnl4RDPFH5zOg5V\nDB5k4fKaPMjw0/hfLnDpBisuJlxWy3ElL27Hhcu8+czZLdzezJMkFzYtb6j4wHAkd2XuIHMll/Cc\nmQ+cXDiUjAVeNjxB2qmB/eCdwlEk1/4rdq6V1OVdh8MPH2/CcEnr/MjrTW5e1oc6BXBe4QMyPnir\nqwunNV1P7vuaGwuPEb4iHOihkr+45SsfzfxmbAeLkHWEV1vzTy0XH3+CZ4qfwVMlfzM2wR8ZC33W\n+czJKQ7X+OUbk91vz5oNuJRbMs8cXiR9fMmOtc28Hj+SNB/rKr/Cz/E1+tyb82pj59u3rG7GyVrD\nR4cfT3t1pQLaYL/CwcfPsCvjZg/CD8bHN+Y+7D6TOh0L/Ow9+V722qtvSJxq9AqrM6/2zJl2j8Jz\ntjn5pW4PFl/MbbCPGRe1B6xvUOOB9eA++NFSpW3IGg38CVvBC94Jh9hQXjmPTflcxxKmhC2YO+Oh\nL/wMni8aA5vjm88bK40Etvinr79ZUhdvWON5kxOdvomvxMLDeW/Cacc/pS5mYG+bm5/jjBubsB4r\nRmov7exypqo5snlV1jJBtM7XkHcdG9a53BPahRnLfeP7q6q4S554q4liHNhoaz5OHCPnPfN4Zd7v\n1MWoa27gc+hFeA5Qu2Vbtjc54ndkPv989s3J2dQmcYtn1qkq9zsx4TXTiQ8/Uvj86Se6KsaALagl\nQjwjdvK5rlWBX3UpD9s886/YtbYZJ/sHzjtxGK0eqHVz+DU+SU88e/Av5ofOARfZbNaPz7V8g3M8\nG+l/VV4zdCLYpqvrk/d/trWtkUBNG0nanu09PdvWJ+AebEZfaCXmFlp91kyeH5/PVvsDP9puNHbs\nMZ7jjotih6QD1eeDkl57GdfsuMS9PyzpAyGEf6P0t8aXXWogzpt3OBwOh8PhcHxRIOh54eRvgn6e\n/93zPEztH0j6xzHGXZL+saT/91I3PK8Vb+s8+Tfs3PV8du1wOBwOh8PhcPQDx2OMdy1x7pCk+iV3\nZz52OdcMX+Tet0v6ofz7f5L0q5ca5LJ+kx9CuLv+XHPyN2x0Tr7D4XA4HA6Ho78YCMv77xJ4QNKt\nIYS9IYQRSW+T9D5zzfskfVfOsvM6SWdijM9e4t5nJH11/v2NukRtKmmZv8mPMT6w1LnhwQFtWTNa\neJmnMieMVMPkpYfnVnMR4fPBDYNDCd8O/i/5qeFW7sqcN3iSGwyXvOZOcw28ffim8CDhuMJFhLcG\nTxYeM5xdOH4zVW5r+MwrDA9wZKj92+vCTDpu8/sXrjT82VVtzut6nFxzYSY2NgHQ7Cz/HO5lyXGf\ncxKv65HbGv4snETWY26u5UOypoyB4dInPM+an706rzG8zAsmBzxcT8ZZ+OWZjwr3em2+Hh7ohR5a\nD47xueTLz0xOuJKsX8kVnccGhxLO8njFE56aaWsK4B8jJu//8axLgBcJj5wx4Xc3Ze3KyYpHy16x\nvstnm/+ftbd1DfAvrp9f6NaD8ZKf3/o/nGO4o/Bti89n/jL24Hp8SOr2NcemSt2L1Adc/fUrWp7p\n7qzboC/GRl91cl941PgFfH/83toEjqvNCT9d8rOnPtbU56F+h7aNEq/yfsF/ho2WZZXReMAjrse2\nytTvgF89N9nqRNh76B0YC7bFv7BtvQeZI1ht8oDTBv7ErcRX9hVxe23O4V1rJFhD+sUWjIvP+OSi\nHO95XzMGxlbHO/Q9xMZhU79jdcmLn+6Fow7/H386n+dr635IKqRv2ih88/yTNWSPsbbUuGA+2GbN\neKtXkjq/GDe2QSNl65LMm+fFDTm2YEt0AxOV3or9S0yjD+IP8+C5Yf2J5/hYtv/IUJs3X+p819ZT\n2JhrOPBcnjD7n702UvoYaOY1VfHWyUXPvsC/qaFR6q+Ucbb7v9OTpfNFd1L51YosX2FfsHY23gbj\nA9hs1NQBOT3R6XpAuSf3P2TiKZiPbRzA/rTNPLFDXYeINRscyBob9n124fOmRtC1ist4EV82xBjn\nQgg/KOkDSnK2X4sxfi6E8P35/C9Ler+kr5f0mKQLkv7+xe7NTX+fpHeFEIYkTSkzYy6G55Wu43A4\nHA6Hw+FwfDEjxvh+pRf5+tgvV79HSe+43Hvz8Q9L2ncl43hOL/khhHcq/RVxRNJ6SQ9JulPS70t6\nhaQvlfTPY4wTz6V9h8PhcDgcDofjShFC939MXuy4Wk7+QUmfUXrhPy5pm6T9kk5L2mwvrothHT92\n7Cq7djgcDofD4XA4HL3wnL7JjzH+ZI/DH6x+78nFjzHeK+leSbpz312ebNXhcDgcDofD0Ve8kJz8\nawkvKCc/xqrQwkIraKOYjBUPpmPppy1shAgLkRPnoymEUURdQ4iIOtEiQOCCn9A/QhaOF2HkYDsP\n5kXfCHgGKs9DNDplClsgxKENRDO24AsCJERQ2I6xNG2WQk1tITEr/mF4WBuBDvNH5Fj/n7DTk3NN\nGwiaEQHSJ+On+A1tzhtxXIzt8WSDgdxHGvcGIzJGRIcACbETAtyjZ6aaNs+Z4iFStzb0BfAXxK0U\nM8HOMeb5GIEn4tq6EM+YKdAWzfitiBr/P5eFw4gXEatxfvPqushaa3fmzHhZOuZZBNLz7TrgI10B\nnG7R8adSqC23UWySr+MO5oVIjvnaolM1sNEMxezmW4H9OiO4ZZ6ITLmen/jO0GC35tgTf2ZPMT98\nFVswDys2L3WVTOKAdK61fynkNtDOvQjpEURbe8S22FTtu/RvC+mMG3E7+5t9w2cEfIwRG9bPyS4Z\nQfpcCgcZgTBCdGxGoTbsMIyQ2viA1AnqmZuNRwBfJX7hh8RSxr9gxKZS5wejpgAbBY4YZyk6lvci\nbQdjs+099vmo8Wcbb7ANscQWDGQdSBhAn8yzRtl7JX4hxG1jCT5CG6UYWR4rsaQeeycCT+NgHdgn\ndj/wuRQEjK2w2O7ZGqwQwmH2wYW57CfDbSEr5o0wFP+bMcXY6vHhsyQ8mDX7GhvNmWJR9DGwxPzS\nPW0ftM3+YD26eNy+hdpCXAh4V452fdD/ptU8/1q/4d1nZBDRb/tM6xIl5DGU5BHdO8jQYOqfvcM4\ngL87X19YlhSaIYQ3LUe7DofD4XA4HA7HxZB4+cv373pBX77JDyF8p6Q9kp5U4uVPhxCejjE+Yq4r\nxbB27drdj64dDofD4XA4HA6HQb/oOmsknZR0TtJhSTdJGrEXOSff4XA4HA6Hw7FcCKpqU7zI0ZeX\n/Bjju6/0nvmFqPPTc4Xbd/TMdHMezhvFP2p+3cRU4gfC7yvc3bmWm9sVSEn3wlflrwuKaRzJfO26\n4Bac1TOmWATctv3HLkjq+LF7Nq/o2TedwZ1ruKGm+A08Za6Fn3rw5KQk6eW71krqNAS0hQ0nehSp\neODJU5KkV+R76dPy+Z89lWywe1OaB3zHCVN4q+YBA6sVeDYXgTp4Oo375s2rJHXFZLA/PHO4sCey\nDuPAyWTb19y0ofQB9ZFCKYfOpLZv25qK+qAD+NzBs5I6/uOuXBgJPPzsOUnSjszVn684lYdN4Sw4\nkn/x1HFJ0je/fIekzs8YC/NfNdrOn75rARD9jQzk4isjLU/7/BTc/DSfI3me+BU+wvXwb2uuLuOG\nR8pn7sXf0Svg4+NmbS3P+dxUx9tkv3IO3i/aBzjI7NGitcn+di5zQ7evT9xYfAcfkDq+6P1PnZAk\n3blzQzPeMcNFZv1iconib+xR4gAF3STpgadT26+/ZYukbk9NmcI8m/L8KFLG/ODsws2n7brYD7GD\nwkJ2DW1BpxVGswJ3em3pK/2s+cB/vf+0JGnf3nXNnJ8+cSFfm67D7+HD7z+ezhO/WL/DeX0pkCR1\n/PfzOS5R+OyxI+cldQWH8H/uZZ6PH00ZlW/eQmGiNm5LHR/8UI55N2T/gJ89bbQgrA9aHK4jbuH7\nJ6vCQk8cT+O468b1yRZ5n3RF79SMe8bE6cGiF0hXPn0qjbUuEPjs6bnGBqWoYx73qOFt8yxiX7Ev\nzpt5zFYFG4nte3PMtgUkLTe9e66wn1PbrGtdEKkg9tbl8GzCzfHFJ/NzkSJ9E0ZjgS/XBR+x26OH\nkx/dsjU9L3jmEI/ZH9uHk22mZ1rOe/GNHIfvf+Jk6eNVu9K+GB5Kc/3IYymmrx1NfaOfojjWp546\n3XMs+B28eXxd6t5Zzptnpi0ohr0/c/CMJGksa4SIkXuz7dj/pyqtB3P7yBMpbu3btb6x0Y5ckI13\nBLSN7Ht8YsHo3uDup3tbn8Pvz0y2fuW4PtAvus6bJD0aY3yqH+05HA6Hw+FwOBzPBcsiOL0OcUUv\n+SGEd0hap5Qff0QpP/7BfPrWEMJbJB2QtFfSZ2OMHzT3F07+DufkOxwOh8PhcDgcy4Ir/WNnQ/V7\nkHRB0o7q2GP5+IAWZz1TjPHeGONdMca7Nm7cdKVjdTgcDofD4XA4LgrPrpMQal7nJS8OYY/St/97\nYoz3XeS6vyvpYzHGx5e65lV37ot/+ucfW5Sr91zhqabP5GWv88ufyLxYOGO0AZ9u0uQSZ47kOYfX\nNmd4zptXd/xT+juduZy0BT+ea+GQws88kM/D0YWfBwdzTZVzFi6uXQLy98OttPOoc9qmz/C500/4\n9lL3lxacaZvX+HTug/NwDRkTth0ueXfT8drJyQ+MpsHm+d9/LHFgWR/LzYcjDjd/tOQL7gwzY/IW\nl9oA423efu7BtkfOwNltc8HD/a45x09nfvKX7E76BfwMm5Hvm+PklWYs2PK2bYnHiY1rrjv3wOVk\nTeFzrjS+iS2sroQ+4bOeq/QY2Bm+PBxcbEI+6cI/hyueucks/kaznxYqRy2amcyJJrc1eZjHjT4D\nvin7nKZKrujY/Ei2yj9XGK0DXHD0DXa/z5s1RmcCT7u2FZzVrZmT2+UUj9k2C81n7I9t2C9wYLHR\nxw6cKH18za1bm2uoacA+njR1FRgTHGm0BvDTD5xIMWbL2s53R00sxO/35LlbTRA/8Q3iGDoO+Lfk\ngK9Ran7ASc+8f/yOeMT4n8l6nx15/NiU9TpZ6TCIGazROPE1j5c+8aOitxhq/Qrn4Xo4y1K3B9lr\nVrtC3zwnaJP9TczgurM9uMo3Zy43z48uV3rv79XgaWPLG/O6TRu9QH0/47XPKvxq9Tgxpd2rrA/x\nlniALqB+vpBvvaxV9uGiXzPPA3yTPce+IX7Rdy2KhN9PHxPGJ3kO4rPbMrfd5ptnPbBRzftnn57P\nbRM3bR0b1tjqecirzxi6GhxdxOq0P23NnHGjsWFNi/4k7491JkaC2la8G+Bz1r+Js8QpfN3WneA5\nznxqDRHPgxHznGYf7836nXUrhj4eY7xL1yC23/ry+L/+/B8sax8/8/W3X7Pzr3FFdJ0Y4/7862OX\nuO53nuuAHA6Hw+FwOByO54IQgmfXyXhetQkhhHtCCA+GEB48cfz489m1w+FwOBwOh8PxosGyvuSH\nEO6uPzec/E3OyXc4HA6Hw+Fw9BfOyU/oVzGsnogxPrDUuYEQNDYyWHJdL5g8tPAg4dmNVjw1cg2P\nDLZ8ZewOpwxeHVw4cqnDgS1cy8wzrNeNvMvw0hZqsrCkwSU46lszT5Y84OSthVM5VHEq4SeOmjzm\nhSeX74FHdyFz+eBDwl2EczlU8uZ3vH/+l9VM4RAy3sxtLTnV55vr4fZyXEp9wi+stRyMm2vgFMIL\nhJ/KPfAYaQEuMsdpjzWQunVgbZkrdmc4cF9Zc3iOJVd8vv/WzJuHWylJL9+1RlKXu3p15vvDZR0b\nabnfrBO5021O/nU96heMDKV74CczXvwfHjlcaPyL88zfjmFlZSv8GK4z9oWrzlwkijQAACAASURB\nVJ7iXnic4yPpPJoKOPDk0+/F26Rt/MRqH7A/vj4YMk/Y5Aun6WdyzvHaBtgf/QUc5OFca2A873+b\nF3zj6jbvN/PdVB1nr8ATh/+LBmXG+AKw+b9pE07v3Ts3ygIud7e/cy71ilsvSevymKxN2ZvwnWtb\nbVrdtgHvv+wX4s5Cqy1gXjZ2ggsVPxuevuU8785+D18YjjTc413k5s9tsz74J9zxuj846rN5f/KZ\n/cHzgLWfL/GVuNDqZWpdDONfKPGIPPjpnjW5L2ILvsk4Z4s+KH3Gh46f67QFtFnXr5A6u5daB/k4\nbd5oct6zV3nWTVe8f/wIHyVeUjvDxkybL39RzZM8mPVVvn9swPNgdKj9XtDmgsdGPANYB+oyoNlZ\nW6057wDsHfycmDZibHEo+30339YXThkdhCTdkJ/DpVaO4eJzJc8VPtearXqe2H5iptsfjIP3CXyU\ndaCmA3ENm6IDYm1Ze3xopnpG8Tv7lbaxM7ZCt8Q8eIegjkddE0jq4pnU+Wang1Ez7pGhZf1u2NFn\nLMtq5bz5DofD4XA4HA7H84qBsLz/rhf07Zv8Kof+hyXtk7Rk9h2Hw+FwOBwOh8OxfOgnXWeDpIcv\ndkFdDGvXbi+G5XA4HA6Hw+HoH4La1KMvZvTtJT/G+FPVxw8tcc29ku6VpDv33XX5CfodDofD4XA4\nHI7LgL/jJyyr8PZiWIhRE9NzpRAHgpKhwbZwBKKhesEQySnr52xREApXIFzhuBUp0ubM3Ejuu+uE\nc4iVELZYcaUdJ8IkxFiI1RANzlbCKVQxCB3pC0EORa0QHiKWYR5W3IQdBivCGOI2K9REkzQ81Ip8\npowILcaB5jN91GLScSPgohAPxxEcYRvETQiuBiiMZNapRimQkts4Nd0W7LACVluQa5AJ5x8IruYr\ncVYRPuX+EUAhhETUhD/RFyJeRINWEDdW2YeplaJdFFfJw0CoPVNEWK1wGp+wgsLxylbMyYoqGYdd\nr5O5LdqOMfWFIIz26mVB7E4hMwRqQ3ke84PpF8SJK0zxHITSrGsRy67qRLHsc0SlC6ZqHOPh8Pks\nHhueS+u0dkUrlsWWiM+krtgS+6MT5rXzpK/hIvZvBYXECwRvk5WAtSu2pPwTgSp7q5VGLWTfGDDx\nyooc61hk9xjzwc/wbWIIokUEn/jhuPGR2uIUAETkSiyzBYKKEDS3MW3Ey4j8ENPWu53xn83rsGFl\nu4b4B/v7RPZdEh7gM2N5fdYawbTU+TNrxriwAfthkyl4NpPdpsSc+bZgWu27iERXGtH0CrOW9GkF\nlBQ3skkVar+aNM8c1oFrWduuyFea12ZT5K74uCmUKHVrx7UIa0fLc2CmGSdjog32LLZGvHmuKk5m\nn02DRjTN+DeYgo5FJG5i+pb8/J+sRLE2JjIvYjhi5enBNnYQD/h85kIbr4aqoMhvxDxbXA1/YiyI\nkLHlCpPUwhauq8ExYgtCe/xsyiTS4LmOqJp1Kck7KoE4a0UfixOCLBqO4xpGX4W3mY7jcDgcDofD\n4XA8/1hm0e31JLztd3ad7SGEbwwh/EQI4XZ70othORwOh8PhcDgcy49+v+Q/JGla0nFJU/akF8Ny\nOBwOh8PhcCwnwjL/d72gr5z8GON7L/faEILGhgc77jucuNmWRwjPMVSk/DXjLecZjtuShSLyZzhw\nY6ZIBXy7etmmTdtDpjhOKa5UeI3tZzh7FJY4lQt9rKt4wvCU5w2PHJ4fhXloC34dPEG4vHMLFc+/\nmmfqI3Moh9sCNIwD3mbH6295nfTNvOGn1jzBjk+aPlMYCM4q9m30CNX8rNaAsUzPLi40Apd1kykg\n0nGQM/832+ZC5v/OGSLhguHESh1HOhjFDpzJ86YAEtcX7icaD3jOEe5+3Qe8xrYwmAVaicLrH6Zo\nWZ5vXtcJowOQujVDYoJ/ZRco40NjwN7DJqxn2Vdz7b6SurVmf8JhxxbM88IMXHCK3MHRDc3necON\nrfvAT9YVzUBsjmPB1YbbO1cKwLV7uPYrOKpW+1P8PrfB/GwcYN9bvmpdaGihcOlbO64xPms51TaO\nMQ/6qAu5oYmgL+wJH5j9QQyBFw9/eNoUBSr7o9ILECtsQZ3C5c7CkvHhVvPBPLAZPrsq319fP9xK\nNxb54lzxZfQK6QZ48R1f22qLuvHYglnMK+aRYiv8ZLQUl2r3LHt0NE+w5oCvGe/9aLXjwd/sWNgH\nxHr8sfarbq+lcaCPQYdgtTf4CGvN+LtCVrlAV2UsbFWeB4wr+8/m3CbPLlsMz9qQeFzHK/YF92IL\nrkEDwrhYW3zUctdtwUSp88GyT3NMHuR5N9g+77h11MRh1pUx1jZGQ0C/RQeQ/Yl9a98hNqxqi/Yx\nrwtG7yN1z2+KexGf5vN8WFPWY9XYYHPcFjXjuVI/hWxstu86807Kv65wRd/k2yJXIYQ9IYS1IYQ1\n/R2Ww+FwOBwOh8NxZQhyTj640m/yx0II/0yp4NWXSzoj6Y8l7Qoh7JC0RtKTkr4kxvhv7M1Nnvxd\nniff4XA4HA6Hw+FYDlwNJ39C0nD+F/O/xzgZLOdBhpO/efNVdO1wOBwOh8PhcCyGf5OfcEXf5McY\n/0jSH+WPH6pO2Uq3H7hUW/zvFAC3DO7YguHuzlb8Uyi1cIpnMkcM3m/Hr4V7ma63fDvOwwOtqWbw\nMuGlwbezvF84bfY+eIPTs4mDCHe5V355OKrwNYPhGNo+Kkpxcx35hQdCNxHmCh+ePqYN15u5Lxi+\nHTy8kt/Z1B6QOp4s3L1j51LObji3Q/Pws3NO3nwcXQD8YNbP8jgl6UTmImJHxlE4uSaXL20wm3Ez\nT66HWyp1+ok8jeKDcBDho0JZpY+yToMtf3jA+F+Nrhpfyxu3+aXHTe5kuKNwLlm/Ujuisg3zoS3s\nT1vT1T1Sxy3GN0ZKHvfF87BcYYD/Fy1LLoLA9Yx3fgFOezu/mvjP+Kw+AZT6GHBI8/HC5TV6GmwE\nn7UGHGjizIzJH4+P0id6mJJnPn+OeV71UK2NQIlP+JOxO212/P/Uzkr2dKXnGBxo/QaQ13y46E3S\ncWxTuPixtx3qHP7cQ07uOcP/L/xtw3mPsY1v3G91A1KnNcENLP+X+DRgagxQA2LaBEd8fnWVB9zW\nZhgdamPDgOGEE7NHjS4mmhhq6x3U47fxdczsaztP62880/icxgP/PeeHz/x4i2mjpxo1a0zPS+3p\n+hr8x2olunmk8/hsVwMmHec5UusXsKtdF/qay1qPeVNPZia2sZN2So2RoS6Y4Aclrg63WrP5AWK2\nmnHznOP5b2uH1MDH4M2zf6kDw2iwwdhwazv2AWNjH9WVW6mjMmf4/tNGp2C1BLZewYzR4NQ6DGKA\n1bXYWhuO6wP9zq4jaTF33+FwOBwOh8PheD4QQljWf9cL+pZdJ4TwDknrlPj6+yTd1+Ma5+Q7HA6H\nw+FwOBzLjH5+k79B0qMXu6Dm5G9yTr7D4XA4HA6Ho4/w7Dod+vZNfozxp6qPH1rywgqDA2FRnnC4\n43DhjuXcvzXfkHPwqeFCkjfb8oLhlE2anOrw0uAi1rmNj5xJvHI4b/wkB/0qw82fNZxqjsNdLFy5\nil8PJ3Ku8JdDz58Ajh/8xpIDO3P8yCdc83JJoT+f+YurjW2gFp6dbHPyy1AO4XN2vG4tAsMl/zdt\nMU/y5dMW9ic/M+uHjZp8zblxm2cZLuKg4QPDDaUJy0G2nMZ6/PjX9EzLAcePOM/44WCuXzncXE96\n/Ho9RodaLneX0z37T2zzxT9zalKStHXtmKSKx50XCH9k3vVcJ+cyFzr3OVR8MvOB8/XoE/BhuJij\nhnc7XRHN8Rv2Dmt7wexJq4dhTeHf2pzjta3wNWtXbFDyxDP1fCt+NGj2D+vGetVznJhpc7efz+Mj\nJth6EYOFw5vaZH/bvdxr3DaP/1K6F7j6XZ78Ns/82qrmBpxtbHByAp8cac4zXxsjGS7XkY+9XvM5\nqwNBB5PFHBP5Xo6T/7vTLaTrWB/mhRZHqupZhPYz++Ho2ek893Qeuw8vtHU0cCP4zTWHeoXhfBd/\nmms1NrZOCcAmxI5jeUx1vnNbm+HMRGprbfan4+fa5wvDI8ZY7cdw0ZV0XHbmZqkDjA+/QptzdnK+\nOY5vDA7neDbfxrt6HsMlrubnW9Eb4dPpevYs+4K9ezg/TxnpcBWv8Cd8kJiCDdiv7MXB+dZG6Bew\nIet1/Fy3z+mjs1meH/t2us1JH01b61e29UBYh7quB2vK3iIG2HoExMhOexCasRUNUrbDuZlOs2Kf\n3yNDua/B1geGzDsR9zHGLfl5Uur8DFX1F0ys4JmE/Ws9hePaR1+LYUlSCOGmGOMT/W7X4XA4HA6H\nw+G4KELvhBcvRvSTk/9OSaclTYQQvkzSAzFGm3XH4XA4HA6Hw+FwLDP6+U3+JyXdKOmopGOSbpNJ\nrdkIb3e78NbhcDgcDofD0V8M+Ff5kvrLyf/Dy7jmXkn3StKd++7qwep2OBwOh8PhcDgcV4u+c/Iv\nFzEm0SRCHf7oOnAiCQ13bEhFHxBBHcoCRKkrCIEAZFIIWtLPx4+cl9QVvTl4Mt1746ak1kK8dcP6\nJD45ksW9w4NdkRzEYE8dvyBJumnLymY8CFisIA+hFEJWCj5RzGnT6k5A/MTRCUnS3s1pXNjinCm+\ncm6yE97UfVHo5vR86utQnufLd60p1yLSQ/CIfREtIYw6ijCKgkhZfHl8Mh1//W0pG9KDT52SJN19\n4/rSB4LNQ6eSHRHDIQy0Qp0iTDJiIYSFjPVsFg3V452abYVg9L1hLBcby6ImBKn0TUEb5vfQibOS\npJds6GyFvW/bvlpSt2aMcuvatHaHsg0RPx08nT5vm0r+hHh0d/a3uogTbT2TbVWL9aRONIdgCqEX\nPrshr9fnD6Xx37x1VXOd1Imu8KMPPHJEkvS3X7GjsQlCVG5FaPWpA6fTfNak+bCv1ldjZRyPHk57\nDb9/8ljyafYJ/o7wCxEm4sczeZ/MXciFliqBPTawxevwjxkjdseX+fx0Xqc7dqzOY0t7uRbx/6fP\nHJQkfffdeyRJH33shCTp7r3JvxEh2+I3Jyfa/U2MoVDM6cp3iTv2msfz/n9J9jdiyuZsM2yDuI89\ngDCR9iTptm3JDx7Jse+pM6nt1+7eIKkTtxMPEAUeyG3cnOPbaC4g9IVnzkmS9mxeWfrAb9i//Pzc\nwbN5Xukzfocvs9aM/1S2HeJFYr3U7RX2CTER4fmqsfaRhZ8Rw7Ed4kHmwV6UKjtnP2BerCVizGdP\npzZP5hj4FbdsktTFNdpB2Pp97/lU6eP/eevLJHV+8OTJtB5fcXNqg28Z8a9SEGmyTUaA6JT9U4tj\n2R/8ZK+xD/ATYiLPkRXZ/z5z4IwkaefGZBtE56enOsEq8Ym2Hn42+dfe7C8kWXj6RNpb+0+182Tb\nMP4PP3FMkvSVN3fZ9RDI2m/9DmP/C+n81rXrmrEcz7ajQFUnHm8TakjSqYm2YBjxij3EPB8+kvzl\njm05ZuQ9+rKd6TlxNre5Mvt0LeIvxTgptGiKi/F5dbHZZPMZXy4FH02RSanbWxTnIrbwvLPnEe2+\naudaSV0MPZr3S3lWTHbx6pGjaY1fke+hOB9i/tn5a//7WbLrOJapGJbD4XA4HA6Hw+F44XDV3+SH\nEL5T0k2S9ktaKemMpOMxxg/0uLZw8nd6MSyHw+FwOBwOR5/hlPyEftB11kg6rlQIa4VS1dujvS6s\nOfmvvtM5+Q6Hw+FwOByOfiJoQP6WL/XhJT/G+O7ndGNInOC/eDJx9P7G7dskdXzA8fOJxwavquYu\nw+WEV/qh3Mbrb9rSnIf/+2/+/HFJ0sszr+5b79guSXriSOLbwWur/+qAUw+/D34cPG24fXAMb1qf\nOLH35c+nMzduV+Zxf8ed6f9cwPOUpJfekHh/v/CRVFbge19zoyTpfZ9/RpK0b1viBR89n7ifN29O\nfdx/MPGGv+bmNN8HDpxUjb3THY8WXiNzZPwfPXA82eKVuyRJ//D3Ep/0x7/uNknSb/91GsP2zIVl\n/nfuSmNCTyB1nEO4qb94/1OSpL/3yhskSb/+iUOSpB97/c2prbwun38mayfWJU7u5tWZ/5l5w584\ndLr0sX1Vuga+6X97+LAk6dVbE09z62Sy8+Gzab6/+NE0hre8LHFD4Qf/jVu2SpLu+2jSFkzd1HFc\n3/qyNF44rhQtgaOP5gF+MDzGP3k8rce33JF8+CV5XeHjnp6oi7K0Gg44xu/55NNpvLdvb47jozs3\npvnDMb4lc7A/l7n5O9d3vObHMi/7lbsTp/J/PJz848t2b2zGgG/8wkf3S5L+jzffno5fSMf3bEx+\nRJGmtVWxOLjd29YlWxBPf+/zaV3+2dfcKqkrpIK+Aj4n3FH40PBS/zLbUpK2r05toyNBM3FL1iG8\n4/c/I0n6gdfe2Mznlq1p3Nj43vvT/O7cluyxp+JnP/B0sucb9yZO8fnZNK77Hk46hruzzU5k/jW8\nW3z+v3w2+Ta+Awf54wdPlT6IIcwR7vT+rBFA1wCOFc1QWlM0Ep85ltb6SzanODZVaT3O7k/2xQ++\ndtvWpk00HNvz+bM5RlKg56HsV8SkP3okfU+z8VC35n/75UnTAeeYePkzf5YKnf/4G9KaP34mzWfl\naLI3vN/PH0p9wNE/dK7VB0nS/tPJ3zeMJhtdmGv55OiQiDWsw8cOJL95Y97fcPn3n03tfepYF0u2\nrGh1IlbzgGZgZ9YKnDo007QJnxvuNOu3Mfur1HGf8e/NuU/iP/5fnj1Ge3Ms89R5/hVtWqXDYD8w\nDrQej2Te/NnpNI/btqY15ZmGvux9Dz3bjPVXP35AkvSqHatKH1+bnzFF53I+2fPOPeua+T1zNo3r\njx9NsSbkgPDKHS0X/NaNq/NYO796fx7Hq/Pz7jNH01p9XX4n+J/70zN1NNud8Y/n+bMuvA8Q6z95\nuNuDL9ucxsHzD3488WhTtuHP/2Xyo5dtT3uMZ9c/pJjURFqf09NZKzHSFaQ7dD7ZgPcQdsgn8v7d\nsjL5AFqQJ06ldXpp3s8UwULfQNytCzY+eTzZ/9c/nrREX7Y3zest2VaAAplodSZzDCKGP7A/2YZv\nu09Pd5z8lbnA1gpTrGt7jvUnKh2C49rHCya8dTgcDofD4XA4+okgp+uAvgtvQwjvDCG8fYlz94QQ\nHgwhPHji2LF+d+1wOBwOh8PhcDi0PNl1oqQ9PU/EeG+M8a4Y410bN2/udYnD4XA4HA6Hw/HcEBLV\nbTn/XS/oO10nxvgvL+e6qZl5ff7gWb3xlswr35+4fK/IHD5yeH/Xb/+1JOn3vufuci/5iP/3v5E4\nxK/dmXizT+ec9p86njhwr9+T2v6Vb3ulJOkn/vgLkqQ9m1sO/t/5tQckSf/uW15R+vhXH0o8eXIH\n/583vkSS9Jkjqe03rk5t/+sPJD7qf/ye10qSbsz88r91Q+Iskg8ZTmOdGx7twMeeSG0+dTLx/f7J\nV+5NbX468QG/cteGpo1vfeVOSdJDmeP65XsT7xw+58998PHSxxeeSVzcb787cYZftinx/750V7oH\nTuVf3/+YJGnf96V5vOH2LbmPdD884gPZxlPzXe57uMG09cNfkcZPjYHVo+lvye/93U9Kkt79t79E\nkrRrQ1oH8rPD/fvf8jrdc3eXgWki5/sl//Wv/kma44d+/A2SpNk8hrf9yv3p/NvvkiSdyVzDI5ln\n/hPvT22/7dWJw/jG2zru8j3Zr+CZw/H8lR/9mCTp99/59ZI6vvynDqU805/KtQN+MusZyIEP97LO\n10xtBtYKb7j7huTDR3K9Au45OtlpOCTpnR9oikjrV/5O8m10AlKXxxuu8FffkvYUecA/+mTinZ6d\nSbb5xW9J6/FnDyce9u0bVuexTDXtPXzkbOnj//5g2h+335D8CT7/G/cmX/3Pmat+05rECSVPeQjJ\nh//+r6c99yPfkHjcazK39TU3bih9YMf3fC5xdv/q0aQjefe3vUqS9E13pC8K4LIeOp3m+z//Kl33\ntlwX4KuGk21PZb3PD//h50ofv/NzvypJ+ok3/Kwk6StzLvTv+93kC6+7sdUx/MpfJe3E4Wybf/u3\nUj70n7ovxYG3vTxpKm7e0PGaj2Y+P/z3zxxJfvOGW9L4Txu9wieeTf6EtqPUK8j3792UOMn78/6S\npN/6ZLL3v/qGl0qSPp59cu1Ysis8cuobfOjRFHNu35LWD173/8hahJ95959Jkh7/je8ufXz4yWTX\njbkmxR157d/00mQzcon/dObov/obUgxcn/1n34/8gSTp3/3wV0mSfuzXU2x/7w99Velj01jaM9/8\nr/80zfEXv0VSp+PZ/Lp/lO79Vz8kSfqrJ9I8/6+cl/7TWcdz6+Zkuy/L64euQ5L+yX/+tCTpndlW\nPHM+9kR6Bo0NprUmZnzFTWl+8MjRI73rI/slSedzjPnSzI+WpO/5j59Ic8887H/xtcnPiZHY6pP7\n03jX5XX5pexfP/i6pDN52y/+pSTpz34sxbn5qiDGRw+l8X7TS7Y3bfOceNXuZH/qlvyLD6TY98aX\n5n2Tn0WPnk7PkZ/6urSHn678av3KtNaffSb57FtyXzy7GM+jOT/+44dTjDi7J80PXjmxhPi1dV2n\nIeJ597J7/oMk6fd+6m9K6vbD1Fz6Sc2GP3wo6cWOnU8xctPK1Mc3vTQ94378/Q9Jkv7gV/9r6eM9\nv/B9kqQH8/PwD/8y6Q92Zr3Vu745xcAfyc+uO37gdyVJH/nX3yyp0wkczHF7OD8bHjvRaT3e+pLk\nJx97utMVSdKP/trHJUl/8c6vldRpPv7iqWTT//Bg1r9lvczX35Z8dmPeC2/+9/eXtj6W29j6aGJC\nbBhP6/Ot96Zr3vN9r5PUaaDe9hsPSpK25bbfcGvSPaBL+XAewz/NejlJ+rPH0nPgn3/gEUmdlu69\nn046ADRBjusDzsl3OBwOh8PhcHzRYMBJ+ZL69JIfQvghSaskfVjSDqU0mr8ZY5ww15U8+dtu2NmP\nrh0Oh8PhcDgcDofBcnHyH7Ev+FLLyV+3YdMydO1wOBwOh8PheLGC7DrL+e96Qb/oOp+LMd7Xp7Yc\nDofD4XA4HA7HVSDEuDyFZ0MIOySNxhif6HX+la/eF//kQ/eXgjUUfDiVBSMURqKQSi1e/GwWPK7P\nopNduVAQBUO4FtEQhZxO5aJECDwppvFfs5DnTbd0BSUQJVGYBhElxVcQslGAB/HfyVwoApEdYi2E\nxBTRkjrB6aFTk3nOo808wICZF8U+ynV5DSke9YbbtpR7D+TCGtiX1Wb8zG99Ft7RJCKup06m+193\n04Zmfueq9aAwDfbFRtjsI48nwd7mFWPZJun83s2tgJDCMIwBQaLUicnGswASUSnjviGLZU/mMWDb\neVPEjAIkm8bS9fv2rit90ObNudgS9sZ/EGFT8OixfP3xyWTDkWzTl2xLwiT2FraWOhHrRC60hUBy\nQ/HlZAPWjc+MgTYZG0WpNvYoFkcxpU8+nfyCQkn4JOuAkJCiX4iC12SfmcmiR4SwUldwCkHj9iwo\nxj8QzSEAQzS6KxcYwr/Yi/jycCVMR+yNgHtNLuRCoRcKC937YBLRvTOLG7EJhYMonkM7fJakn8+F\n6P7Rl98kqfM91hhfxo9Y8y25yB1rS1///fEkWtuyqiuS862vSNRECpntzf3/ySNJ5PoVN6b/q7ki\nrweCb4ozHcxtI/hmL1M0TurWCrzrw2le370vFbujsA1ri1AbQSvzOJv96hO5mNdrKyE0Mc7G10ee\nPdfY6mwuXPXSHcmfLuRxFjF5HkNXbKcbN4XOHsl2/tTRNI63vWp3cy3xB1uxtrvzfmGs+PZsVTis\nCFNzQafjWRiNvRG/81zBp/FZ9g97mbh3/4FOcPnmXJyI/ufyfmD82A6fZr9QiI+xLOT9Thw4VRXW\nY82IDYxzfxbBfvWtWdid7zmU9y8FBSludDjPdyLbdM2KzncpCMheRJR7U97/T+b5FV/NRbFevj0X\nn8q2LULjLPQ8OTVd+lg1nPqjcFO9VlL3HMGvWC+KlZ3Jxxk/NkTMXI+fuMt7B2v5B59LwvVvflkS\n62Nn9snH9qe1/dK9G/MY0vm6SOf+Y8kWCITxb57fxAiKmNkYvim/82xYybtPuq8uoGkLUzEfCmh+\nYxZGs9YPn0hC4y/LMYZ1YqwUVaxfA+mPJBH47sefTntx3Wiy65fftuHjMca7dA1iz0tfEf/5b/7R\nsvbxva+98Zqdf43loOuAuyS9fhnbdzgcDofD4XA4rimEEN4cQng4hPBYCOHHepwPIYSfz+c/HUK4\n83LuDSH8wxDCF0IInwsh/NylxrGc2XUmJI3VB2rh7c5du3vd43A4HA6Hw+FwPGe8kLz5EMKgpHdL\n+lpJByU9EEJ4X4zx89Vlb5F0a/73Wkm/JOm1F7s3hPAGSW+V9MoY43QIYYsugWX7Jj/GeF+M8T3m\nWBHebtjowluHw+FwOBwOxxcVXiPpsRjjEzHGGUm/q/RyXuOtkn4rJtwvaV0IYfsl7v0Hkn42xjgt\nSTHGo5cayAuWJ38ghMKbljou6Fjmq8G/g19c85pfsi1xPWcyVwxOIrxeOHDQzCjEAY+NYhTwNl+y\nPvEF11dcRM6RaxUOH/zS2fnUBjxZ+JmMH14hY6EgSU23hz8OL/BC5nzCmYR7P5/nCf8PziGcavC6\nPRubvqSO5w7QOMDfhB8M/x07r1qTxgR/lSa5vy6+xF/Mq8YyHzZzCeEavi7zGCcMxx2+KVx31gku\nY61NWDve2oRx0xfrU3PTJWlqNh2Hv4k94B3Cs63nxJzhLx7NXMqRwcTrxO/grsKxhIt52uhKai47\nHFy0BV9566Zm/Pj9ljwWxnAyjxdb48MvzQWTVPnVQBhpDlku/sgS3Gq4u4zbcqfZP5J0MvcPbxR/\nAtj53GS6Dv44fcORhfOLf9Vaj9nMF78180YpzEZRL3QA/zIXIWPDsy/qymVgwgAAIABJREFU+CJ1\nnN+Byq++IRfjYx7oKtBwDJlYwppzHE47/NV//FWpcAycXanz49vzWnHvXTekwjRw7LuYo6YNeLjF\nF7Ltsa0krc1+xf58882b8/Hh3IbyOMeb+eF37A80HzflYl5DVdxlr6DBYf/esWNNHldqc3U+j14J\nHdLgwHC+f6ixy+hwF8e6NpOt9ubChRw/dX62mc9CboO9Z/U8FD5EeyR1fnzGxDz2JOOdzrEDGxHr\n0YQQ67HxndvXlz7gid+edS7YxvKt4UKfz/sAvQjPAuLb2bzW2L6eIzIW9hxtYN/BPD9iI/OfLLqx\nNO8VRVPR+ZXVOKBF4VpswnW0TQzdkf0KzQE775U7Oi0UsQCuPb5JAb3y7DFaFebPPj9NvNiefPdM\npeliDWkLnj/XfPurknYFP2Hcg9nRXr1zfdMn/PmxyneJUzzf6PPpE2iKhhvb8PnhY+fy5/Z1DF49\n8Vjq1pQ2GN9rbtjQ9I3f887AO8UhU6iR52Ot6aEPYuFcLny5b3eywfVQ7TVoebnol4Edkg5Unw8q\nfVt/qWt2XOLe2yR9ZQjhpyVNSfrRGOMDFxtIX1/yQwj/i6RPSBqJMT7Sz7YdDofD4XA4HI5rAJtC\nCA9Wn++NMd67zH0OSdog6XWS7pb03hDCTfEiGXT6/U3+SyV9XtLfDCH8VozxSH3SOfkOh8PhcDgc\njmVD6P4P6DLi+EWy6xyStKv6vDMfu5xrhi9y70FJf5Bf6v8qhLAgaZOkY0sNst//R+O4pG2S7pc0\naE/WnPxNmzb3uWuHw+FwOBwOh+MFxQOSbg0h7A0hjEh6m6T3mWveJ+m7cpad10k6E2N89hL3/hdJ\nb5CkEMJtkkaU3ruXxLLlyb8U9u27K37kYw8W7jqjgG/HuOCM1Tww+H3wGgdMrm3u5Th/0DFVuL6L\n+G1VJ3D34ESSKxZuKH8l2rzaaAiYB7w2OJbwOOs26Zd85IyTNuD28Rmbrcw8ujkznxpwt+HkMU5s\nBIdv1nAWsRmcv8Ihze3WPWELmxceOzN+1oN82t396jm22jWxH/x3dAfzC+1asz7wz2kimnXr9Tf+\ngvEb2qZveKisMfOYW2j3ENxRO6Ze/cOL5VrutWtJH5xnTFxW85q5E/0CPscwGA3rM7QEyTIYO9Tz\ngK9s9ytrz/jtfqCJOaN7wP9Gq/1h/QFfhmeK3zN3dAFwjBnLjJln/Q0PHNvVZi8BOOlL8VA5j7ag\n15oXPzEaItbFznPa7JfiT8am+HjdP7aYNTHDxhCrY2J69AEve1XVB3bEgQaMUYomyuxnrsJn+Twy\ntPg7JnwSG1h/quOn1NmUsTHvdZn7Tr2Sen+wxmiGWHv7nLAx28aBsu/zj+kqvzv8anyNcdk9ybMM\nW5D3nD7gjMPfruPCrKlxwHh4ZtEX86FP9u48MTG3Rzv1F6BLxcBBE+MsDx2ft34F8GGp82/GZWMD\n82Es7Dly2aPRwZZcX9fWwR+m8jyIu+gpbAzHb9CXsDfts6wGe97GbvwMm3CadxvmzzOWodiaNVKn\ngZicMc8N8y6Eb7BuzJNZ2nclbCfV7wLZniZ2Y9/VY4PXbJ74vXe8Iv7kb/23Ze3j7Xfvvuj8Qwhf\nL+nfKn3h/Wsxxp8OIXy/JMUYfzmk4PDvJL1Z0gVJfz/G+OBS9+bjI5J+TdKrJM0ocfL/7GLj7Dcn\n/+2S/r8Y49wlL3Y4HA6Hw+FwOL7IEGN8v6T3m2O/XP0eJb3jcu/Nx2ck/b0rGUe/OfmHlPj440p8\npQ/UJ2tO/q7dzsl3OBwOh8PhcPQPQb3/b8uLEcuRZSgq/W+ERfk7a07+ZufkOxwOh8PhcDj6jLDM\n/64XvOCcfMf/z96bx1qanOd9T51z7tb7OjM9PT0bd0ockjMtUY4AS5GoQAsTQUgEOXYU20EyUSAB\nRpDEieUk/sNwkMBGDDlRrDRsAVosyBIcAbRESRElhxZNa0SK1DIkxWWGs0/P9O319t3vPZU/qn71\nVb3nnNu3u8+d7ibfZzC4fb6l6q233qrvu+c+z/s6HA6Hw+Fw3DtYmAl3LSf/8fc+Ef/uL4ywXaaK\n/+Tsmbt2/DWm8k1+puE4HA6Hw+FwOBx3FCHs7f/3CqbFyT8VQviIpCck/YakxyT9boxxaUrtOxwO\nh8PhcDgcjl1iWpz8L0paV8rXeUXSE+Ne8EMIT4cQPhNC+MyFxYm5+x0Oh8PhcDgcjltAUAh7+/+9\ngql8kx9j/BX+HULYJ+kPJ1x3TtI5KXHyp9G3w+FwOBwOh8PhaDG17DohhA9LUoxxRZLnyXc4HA6H\nw+FwvKUISi+3e/n/vYJp2vruEMLnQgjfIempKbbrcDgcDofD4XA4bgLTLIb1hqRf2+kCL4blcDgc\nDofD4dhL3Eu8+b3E1F7yY4y/Wn38xIRrnJPvcDgcDofD4XDsMab5Tb7D4XA4HA6Hw3FH4d/jJ+ya\nk4+wdtrXOhwOh8PhcDgcjuniZr7J/6YQwockvSJpVtKXlQS2fyTpvZKelfRt+efbJX3cNuCcfIfD\n4XA4HA7HniE4Jx/cSnadoaSvSHpBUpQ0L+mopA1Jz2mHv5LEGM/FGM/GGM+ePHHyFrp2OBwOh8Ph\ncDgcN8Kuv8mPMf7UmMP/MP/87emY43A4HA6Hw+Fw3BrIk++Ygh8yBcfhcDgcDofD4XDcJZhGdp1T\nIYSPSHpC0r+Q9JckXZb0szHG6/WFzsl3OBwOh8PhcOwlnJOfMI2/aHxR0rqkRUkPSHpG0hckHbEX\nOiff4XA4HA6Hw+HYe9z2N/kxxl+ZhiEOh8PhcDgcDsftwr/HT5iKNiGE8EPTaMfhcDgcDofD4XDc\nPqZV8fZ9IYQDSmk1v0sTOPkOh8PhcDgcDsdewin5CdN6yf+UpPuV8uY/I2lLiZPvwluHw+FwOBwO\nx1uClELT3/KlKb3kxxhHqttOuO6cpHOS9NRTZ+M0+nY4HA6Hw+FwOBwtpvVNvqT0TX1+kb8hoqTh\nMIo3/eEw/WtmkGQCW9tDSVJvh7+5cG+M7e8L/V7AHknSdm6b6zhOH/b6+tpBP9mzuTUcax/ATprI\nXSo3LUwcVrbSH9dsbrfjmOmHpi1rEzbYzzUYG20wQmsn1+Er6/Ue7eTz9Tisj2gbn3AlfXDdoG/n\nR2OP121hB2PlGtqeNH7aDmY+alif4G/rI8B5jq9vJpvmZ/tjr7Njqu/d2Grn0s6TPc884Ktx6cLo\nq2fGxXEbXzZWbZN1fA567dwyxu1hG6N2nHat2rVYu8f6mzETA5yeFAO0Tbx1x0evsfbPzbRzaPcj\nGys02euNxi73zua9Axds4ateG9s27ibNXz1e7sG+oYnNLhbaebNxhj/WNrcb23YD7rV7ud13ra/r\nPR67bOxOWv/EmV1XwNoy7tyW6Ys28UE/923HM9JedZgRbZt1Gs0c2ucJPrrRPl1jPdvJvf0J68PG\nkwW29cbElYWdD3vd1jZznj4Xn24kW+s90q7rSW31zZ5j18WkWG7HmC5mLyO+Jz23bYza68bBrrm+\niVk7xxvm3cKOY6vad20c27UEGN9C9rN9rg+Nj+v7sde+V+z0LnY34h4zd88w7aJgp0IIHwkh/GQI\n4V1TbtvhcDgcDofD4XDsAlP9Jl9tzvw1e9I5+Q6Hw+FwOByOvUNQcE6+pCm/5N8oZ37NyX/SOfkO\nh8PhcDgcDseeYNrf5N8Utoex4uyN52sGeMMVZ2wS16pwqQuHr+snnW+5l/BuLWdfkta3Wh7aJO7k\nJF6gBXzD9Y2OH7ww23I/Z7LBlhu9Co9xptd8hm83LPbn8VbjgO+3b27QXDuJezhJGxGH7XU1V7fj\nx7a806Hh4tK2nT+4f3BLkRbUHMDl9S1JHV8QfrPlgNvxMP5JHGvLTZQ6ju5c7sP2Vfow/Fp7HVzw\n2YpHaXnidG8568Vn+Tz2W6708nr6vK/iuPYM6RZOJ8fh4hPjc4YrXjxiOLq90PnKeq3oMra3m/Ew\npzG2fGE4o8McWLOFwz+qX+iZPcICXzEeripTy3o340z9Ww1BauP6Woq3/XP9xm58yfrol7Xarp8a\nM4Y3bucaX2xtpp/MJec3tludAH0QC/U9tvuOlzyeg0scsU6wDV/VvOayz+TPlhff9Zl+Lq1uJtvy\n3oPjLV99WE1rz/iTxjbQTw3b/Xgl7wsLZvyWbx8rXYbl+bM+l9faPWbOrOdJOoVxfPnN7XbfsXop\n7B+amBi2w672i9T3xlbXht2fyjOrrON2bmcm6GTAuOcgPloe8XOeO/YWowWxWiGrIapRnsvm6Wn3\n7m4cbdvE1ZaZp4Pz3evNiO7LcPE5z/pGM9Dp5cbz6etnLdqNSZqC0sdw2NhgefbE+qDX+lTq5oG5\nx++swQ2ji7Nxx15i9WT1s5Yx2j3QPkfudjgnP2FaxbA+bD5/Zwjhjv4C4XA4HA6Hw+FwfKNiWi/i\n8yGEvy3pk5K+XdIb+d8NGk7+GefkOxwOh8PhcDimhyDPkw+mnV1HkpYlPTfuRIzxXIzxbIzx7ImT\nJ/ega4fD4XA4HA6HwzGtYli/LunX88dP7OaeYYxa3dxueHNSxzfv+MEtP03qOKw2zzfcNcutnId7\nD9/fcPPpq+bGXby+kdo0ebMLhxWuGxw/eM5w+vL18NqwflwfB+ZabQCGXVlJnNYj+2baNjL9+uLS\nuiRpf+a8wterebnwNclPTP+W4w6fEBv4aXO+w7G+stLxge87NJd8stXymQ9nu2mLe7HJ5k5mgLRT\n52VfMHbAIWReaBu/940P4avCXd4ew6GGiwg/HL774X0mP3bhXqoZ36Df2sY4r2Zucm2fzY0ONgs3\nOn3GOjih+PbQQvLtfhs7kra2Wg1K8W8G8QKv8/Iy3OmW843Px3Gvi1bD8JHRjeCrLl925nnm+TiQ\n1/2gB/e7tb0ec9FTGF5t4WlPyGNO35ZbfXWlm4+D2Y9Dcw57u/Gk64mbtTyn+Ghlo9Ui4OMam5tt\nLQA40swTbWCv5SQXXnT+PBe6+Sj7peEnb2WNBPHPmsS6kps8c72tFiQOO06vrYexWea25fkS93Dx\nidm5mZaHPm/GVduD/+D10yZzvrrZxvS11bzPFg517mNMbvue2fixq2ij8j0XrqX99fiB2ea60doO\n+XlS7bubRRc2Wruk9oHl3luNB/Fo11ftg6U89mPZzq6eQqvHYo9kSokRqxmp0evzvCuLLt0zUncg\nj9c8R66ubDQ24culak9kru2ewv77Zp6HB48uSBqt5bBmNGrsjfU6nzPxUzQs2Rfswjzv0HrgEbue\ni3aw2q+u53vW8jrnuUh8j+qv0mer1SGWeTca9330VpmrrOtZH+bxtVqbbaNpZK4Zp+Xwp/43m2sB\nvqv3nbsWwTn5YGqc/BDCX83/dj6+w+FwOBwOh8NxBzGtX8kel3QwhPAT+fN/Oe6iEMLTIYTPhBA+\nc3FxcUpdOxwOh8PhcDgcCSHs7f/3Cqb1kv+8pBdye98h6Uvjvs2vOfnHT5yYUtcOh8PhcDgcDoej\nxrQ4+R+XpBDCX5b0GzHGscLbGkFBs/2eVjLvDr4jvDT4eAcGoyaWvMCG4w0Xen/msm0b3mM/s9tW\nc85hmwt3o8qlTJtwhy3vH87bVcOfg39acuD2W34245U6fiIcuMILzFzVwwuDxj7uvbSceI4PHUsc\nxcWl9BlOYs2txK/w6yw/Fg3EwTLOlpcJnxAOI+2cPNhx5OFXwtlFQ7BlOJ4l5zO8wX6b1xhfB3iD\nM929cIrhpNq4sXRSfHA48zPRTNj84LH6lRyuLkeYM3wWC4e39bPNu23rAxzdP1v6IMasPSUPu/ls\nNRV2PJeyrgOudW1nqfGQ/Wk5xbaWAPPWm7H5s6kZ0TmZuUYXArd93dR84A78zRxvmDoBYH+l0Zkd\ntpqGToeR84P32z2Dpmxe8xmT8x3OrtT5G+0G65U2aYs5D5mXujZsfQz4tFLxzFnPllvf75EPP113\nIPN+2VNY//iKmO/32nmt22ReGAfjmjd1FuZN/nb0D/iQfeFANR/EWpkyw8G3OeHRRLB30Af71Hrm\nLte6FPsFGXvFbDZjXK0DqeOnc3zePE/qMgH0kUs3lL0F3jXrp+gXTG53m3uceZmprmM/sroKfIiW\nhrETy6tm/ewzeenrvR1tBhqJJZPX3NrLnfiQ54jdr1crDQ8+OZqvYTzYa2tTbJuYmB2M33MWxtT1\nGJoaFIz51JH5pm/mtuyJRvsF//zQQhe7zNEho8Ep7wa8d2Qfbpnz+JA+0M/Ue8nsQtaa9baae7q+\nWi3BgtGPgRP5vYBY2Kq0aczN/YfnGns0YN2rsR8UzYepxzBj8utL3fNqc6uN801TV+Vuh1e8Tbjl\n2Qoh/GgI4e11jvwY4y/FGJ+zefMdDofD4XA4HA7HW4fb+Sa/J+lHJH01hPAfSZqX9LKkpyR9fgq2\nORwOh8PhcDgcu0ZQ9xf2b3Tczt9dzktalHRR0hV1fw0czcOVUQtvFxcv3EbXDofD4XA4HA6HYxJu\n+Zv8GONvTzj1iR3uOSfpnCR98MmzE38ZcDgcDofD4XA4bgXOyU+4Y/nsQ0gCOoQjFIBAvLRgRLE1\nugIO6aQtqoRgZ0SklduaM2LGnhE5Sp0oBtELhSA6MWP6iTAKsdaBUnikFZkhgKtFK4jzEO/gi1LU\nKn8ugtRsP0U2EP8g4AUIsKRO8IjgBn/i1lJwI7R+p6AH9iL4wUfMV32PFXJ2osVWRGZBsZZgRYzV\nRwSdjIM2EebhozUjXLucxWWl6Fpuk7k/WAmn8DfhwhzzE590xUzawkP9XluEKdd5arYaxhHUHxmj\n1Pm7iM8Qk0WK+7Tixtp+EEyMrm5k+5iPAULV9PMkxcwQFOap7YTpuZ2qABF2zhth4D5T2IbYsMXj\nmB8rcKsL2BC7rEXaYG1xHrEuc4pQ0hYBsrbVbcwYcSWf8OWGEaEdyeK0EuNGaFiD/Wcxi5QROhKr\nPdMHNtE38zWb1zlxVwv27B6GXaw52lo1BbdYx1xHmxRGq8eDoNMKCGkTcwZFzJfaXs2iRwR92L9c\nxORjCodlX7CuZ8162By2a3HDXI9n8O129Qdmrp0xCRusCBOxL/HWFXZLx2kRX9UCSQocIjamzaP7\nW5Hrdr5n2GsTAmA/Nm4am6VujmeL6DInJTB7dVeMcNhct98UKysFEGe6fRp/r5niY6x3fInIlVig\nLfrgs93XpO452CvPqjZpAmAvIVEFc93ttyR2MEX01PkbYS1zxmcbZ1u8S5RChwi523HXsHYxRPZo\n4n59i3Xdvp+wf5W1R7G8Yed72sbPdq8H7IHEds88s9ZKYb52nqTu/cMWEbSFJh33BvZMJh1C+G/3\nqm2Hw+FwOBwOh2McPE9+wp695McY/4E95px8h8PhcDgcDodj7/GWJjyti2GdOHHyreza4XA4HA6H\nw/ENgLDH/90ruGOcfClx6eCEwemD+wZ3DF5bywNr+XCWowpvuXCs863DwhFvz/Onl7r4A1xjWwDC\nFsmZNwWG4PvCiWN88AkH1Tjg4pUCI7mPQ4aLOFKQJx+fm2n7pL26MFJdlEfqeMmdDbFpE39bnj2f\n4T/W2gLunTU+irZiTQZ8wMKBhRNrxttv+MCtLoE4GVD8Y7st0gKOZR4zbW8aLnsdVlvGXii2HLaa\nAuZj1hS/Ij4H/dHfobkHOyxP3BZfoQ17HRzTUtRrjKuxp+Nlt+PZMnz/wmM2nMvCUdYoH3jSuoD3\na4vH2eJfIzzoymeWWzvYbvUljKPjzbY81IEpbDU0HOYaVpuyWTQ4GnvcFt5hnuw+UOPEwbnmHnxX\nYtnoZ5hSfMi4+v2Wp123xZqbtBZDaNc1HPG6yJIkDcLoGqYtWzyq2zvagk3E7MIsWpXWJ12xvy7e\naMPqYtaNfsHuida3Iz6rHspwizt9TO4LPjZrr8f8tPNitUPDEvOjx0rRqE30IsReq1eaxK0GtiBX\n3ce8Kc5nn0kzZl3Q0oLRYdiifqmN9LMrStZy6vcX7UE7H2Wf2sI3rY6j5v1PKphlKRH0adcqz88y\nPxrl/fNMhG9uYxRQzIs+Cs9/qy3cZp9H0qhGgDkrz7UJzxHMhItv9/K6IF3Zh4w99l2mazvrADbG\nv8fMm/et+hp8VNbihEJ0dyOCPIUmuKVv8imEVX0+nX8+Pi3DHA6Hw+FwOBwOx63hVr/J35b0IyGE\nT0pakvS+EMLDkl4IIZyRtCDpt6P5KjeE8LSkpyXpzJmHb91qh8PhcDgcDodjBPcWpWYvcauc/OuS\nNqvPQ0kvSXpB0lclvc++4EuGk3/SOfkOh8PhcDgcDsde4Ja+yY8xflTSR6tDn+UfIYTjkv7wRm0E\ntTl/R87DpRzzy9g4rrMkzQzaiydxiyd9HrRUudzm+L7GcSR3sm1c27vFjfLSTupTGuUcjmLntm3f\n+Hg0O/uYlifkmVqYkC8f2HlL/e78++hOPqgx15vcd51X+WZgx7mTLfBPLS98t/ZbLcJuMMl3s7sk\nLTIf4+bFwq6LnnZeY/Z87Uo7V9ZHNrxsW2A3eZ2t3f0JcWKPT2p7p/ks/pRdW5PuGd/HjdZEax86\nhfHXTdrPxl0zaWyT/B+MRmInTGrDrs1JY5/k2920Cez4brQ2x60LGxc2lifF1ySM20utD260Pu3z\nEYzTqIxeM/7ZOnnNTfLtuGO2/5v7Btbav5Mf9t/geTg/ISZ2s9a69TH+/KT91i4LpnqncVh7bvyO\nsDtb0rU7j+OeQLi30lzuJaYuvI0xXtQOVW8dDofD4XA4HA7H3uItLYZV58m/4HnyHQ6Hw+FwOBxT\nRtjj/+8VvKXFsGpO/knPk+9wOBwOh8Ph+DpDCOF7QwhfCiF8NYTwP4w5H0II/yif/9MQwpM3ce9/\nE0KIIYQTN7LjjuXJj0q5XTdyPt2SKz3/ioRs1+ZLrkFuXs5xD1w2mz8eDE1efVB/hgNJfuPZCTli\nbV9FS9Br8wL3xuRBtjnDaXuSD9Ayb03IjzwpL700ynskz27Jy27yrm+ZPOA2T/44vht9bJUc4zmH\nr8lZbTE0eebH8QsZG0O0vmMe1jaSv+Es0pLto9Q/mOn8Qr7iaPJ9cw9mMc6NrTbvt43Rcb6yMWpr\nPEwCsc5VzHTPxEqyr22r5JOf0FfJ+W5qUtgYrnPYD8182D5pi7zyGDxv6i/Qx/LaVtOuNJqLutSV\nMPFFzHId82XXA/NRj9/GBafIBT9j8kjb+bPTZtLNp2Mmh/WkGLbzMprzmnHkfa06R25ralBwDTm2\n6WtgcuzTxsCsoxL7o8MpmJTP3O4ldl+wvm1qVUyo/zBj8rPbPWVo5rpnniereV+QOi5+yftv9mjr\ng60JNS0A4xuOmXw755ZnTd82vzy20GJXf2L0+QHwnX2WDkyufvu8sc/Y2pWbxh4+29z2HMc65pa8\n9NQlsXtm3ZaNuUlzOul5MTR53ev56PV2vtau73LdhGeqredTj417bb58e9zuIVZ3gT/qehjlPcTY\nDWiD8Y2uRcbV3l/vPaxPu37BuBogdxuCJr9vvCX9p4IkPy3peyS9IunTIYSPxhi/UF32fZLekf//\nkKR/LOlDN7o3Z7D895SS3dwQb2nFW4fD4XA4HA6H4+sY3yrpqzHG52OMG5J+WdIPmmt+UNLPx4Q/\nkHQkhHBqF/f+Q0l/U+33PBPhL/kOh8PhcDgcjq8b3GFO/mlJL1efX8nHdnPNxHtDCD8o6dUY45/c\n2ISEPaXrhBAeiTG+WH3uimE97MWwHA6Hw+FwOBz3HE6EED5TfT4XYzy3V52FEPZJ+kklqs6usacv\n+fULfv58TtI5SXryqbO7+lODw+FwOBwOh8Oxa+w9JX8xxnh2wrlXJZ2pPj+Uj+3mmpkJx98m6TFJ\nf5K1Fw9J+mwI4VtjjOcnGTm1l/wQwn8naT7G+Hd3c32MSUCHmG81i4IWsgBpqFYwUouMEK4g9hn0\n0jAQvly8viFJOn1sIfeVrl9Zb4VH/PzCq0uSpLffv7/0wTkEW1aQQ18r+fyhhUHT19CIm/r90XEU\n0WG2a/9cv/k8Y+4pRT8QNWYbENeME2Fez8KnA1k0Vuw3gkjaCDMtg+vy8mbzmesWqqIh+GD/XHOp\nXr+yJknal8eFeJc5v7yc5gmhz6kj85Kkqyupz1rMibBwfbMVDM5nezfyce5ZNuO+tpo+EzOIlus+\nEBitbuS5zW0sZVHooyf35/NpvIibXsvjfMcDB5r7QP3bLGLSSSK4a6tp7My1PX8l++bo/iRk648p\n1EOIYSdtEU+IM8vaioj60nUXl9YlSScPpQnFQ/hB6uYDexnXVfOZe+7LbQ2NmIx1QmzUwsQrOT4s\nEO+yBq9lXx7Zl3yyNcziQESm7BMIJKsFsm7EcttlLdnPaj4jBEUwzLgfODzf+KW+FntnjR2DfhrP\npTxe5gEfW+Ee83d4X1eSbjHPGfFODNg9b3Ob/SxdcD2fP8z+laf4/NUU0w8f31f6wF7Wu90LiTPm\n/rk3rkvq5h7xpRUU1uJ3Zn91w9qdrr2a1zHxz1pDDHh0/2xqJzfZid+7uOLai9fb2Hwzj/nEwbmm\n76U1EjhsNeNnPZUECFVcET/09brZI2ycvXZ5NfkoC6WJVczmmcB5SXoj23vswGxjh50P9po3r6UY\nQUB8IO/L2MBamKsKLeE3xPckACC+7J7O/nRkX1v0j/2Y2D1axS6xiJ3s1ScOzjafib9jeY55zrM2\nbXGzQbWXgE7kmn5um+QI2M/6ffy+tOdbEa0Vzdf/ZozE7NK1Ns5og1tfu5zmEV+V52UeT72XsOZt\nPDA/zBfPxSXzPJkte0orwB+3BpnrXkg/ieHdFE9z6NOS3hFCeEzpBf0vSfrL5pqPSvqJEMIvKwlv\nr8YYXw8hXBh3b4zx85Lu4+YQwguSzsYYF3cyZJrf5H9a0neEEL5Z0vfFGP/+FNt2OBwOh8PhcDhu\niHAHs9nHGLdCCD8h6bcl9SX9bIzx8yGEH8vnf0bSxyR9v6SvSlp2vZAmAAAgAElEQVSR9Nd3uvdW\nbZk2XeffSPoBSV8Zd7Lm5D90xjn5DofD4XA4HI6vL8QYP6b0Il8f+5nq31HSj+/23jHXPLobO6b2\nkh9j/P/yPz++wzWFk//BJ52T73A4HA6Hw+GYLu5gmvy7CnesGNYwRq1tbGtp23AsDd/x869ckyQ9\ndl/Hl4fzdjJz9uCdci9cuAuZg2iL5FzOXD74dl2Blc6+i/AXZ1u+bDRcxOOZD/nC4oqkjpuPTfDr\nLl1PNh3PNksdFxKu24NH55uxd3xUOIttEQo4oHDYAbzU1G+yG74mvEx4zdj7Z69elSS9477EGbWc\nUPiOy7m9urhMV/hkO9u53fiIvuH3w1NlHh/MXHzuh78Jt1eSBr22uAc+e+TEvmwDBWBaDvtithc+\n5FfOX882jhb0ePa1S5Kks48cldT5HY7rhuGswsV/5tWLkqS35XiChw4nFE6pJJ053hYtWij88jR2\n/A5XmviCcwl3l/uIWeZVkk7nOILr+lKOzYeyRqVwX+HF5zZOVrEpdfxUfFnP+WbmcBIXm4Z7/O5T\nByV1XG/6xEr4tPD+lV30/JvLpY+jeey2yBDcYXiyxDDxA9f1yjL84Jmx7UjS82+k/h7OcQQ39Uuv\nJZ3OEw8fbnzBmry+lAwmtomvN/I8oK+p7WS9oz9ij7C6BUCMsPfAv8V3y5X24+JSOvb2zPnmWrQC\nB2fyZ+Y031eK/R0kzlKfzPVKNefEHOsSP3Mt/madnD6a4o2/mrMf940Oo16L7JdvXE3Xsl8WDcgK\nmg/iv90X2K9Yc+zfdezOmkJ5cJ7hOzNm+jyfY5o1+FjW5lBojLh79rWrpY+3nUjzwL7Etew/rGv6\nRAdwPcfC9jC1iQbhTF672CBJS/kcbTAOxsXPV/Oegd/xyfnsY9Yo2pF6zln7hUufn9PrZk+EA/6V\nrMN4f14360b/wzyM48vDVWduiXOeYW+7P/n0pYtpP2Nd7zM6J+LzC69eK22zz7AFHMrzwXPw5dwm\nWDBF+165lHyITqDThHXrnBExtsWlNB/ER9EvZD8zri8v5r3mwSOSumJm6JMOLXT6Be5ln2Xu2UN4\n9l/I+8Fs9in7NP5/82IaD3FY8+xfuJB88UB+LnMODV39Lua4+zF1BUUI4cPTbtPhcDgcDofD4dgN\n7nCe/LsG08yu8z8qiQS+EEL4bkmKMf6uuaZw8k87J9/hcDgcDofD4dgTTPOb/ChpW9JhpV90VkYu\niPFcjPFsjPHs8eMnpti1w+FwOBwOh8Mh/yo/Y5rC27/Hv0MIH5H0r3e6vt8LOjA/KDxt+GlwzOBW\n/+HrlyVJZ6p8zfj3ykqbk/voTJt/uuQzN9x1OHzkgn7m9cTFvv9wx4ldKFy91WxPOg4fDd4dx+Gr\nkbcZDiZcS1DnvH0958eFnvhc5gfD7YZ3R352PgP4gnAYtwyHXJLOL6V74GM/dzFxJt9xMnGm4WvD\nvYSneeb4QtPHR59NdRy+6+33S2q50/gNXvCpzAmHJw6PES5+x5dP58kdTc7xL72ZOYqnj5Q+iAfa\nxL/wZr+YOdTYAv+Xtpn7kp89j+tylYv9mx883PTxq8++Jkl6b/Y/Go5/+ocvSJKO51j92qV0/eWS\nY7nlWNc5iPEb9tMXbcPJnem3uwic2BeW0v2sE/QQ1BiQOg4ogOv6tQvpXupHwCclhr+a4++lq+nn\nBzJH1OZ9rv/9W195Q5L0w088JEl65XqKp7kLyS7WET/hjGLjgbxGyX395koX44/nuhXEBRzdEFp+\neb9wV9NxOKQv5tiGr33A1GuQurkhzlkP73owrQ+4uPB/4cnS9lOPJf3Gsy8nPjZ87ZovTxuP5HNw\nqYm95a30Gf7yodwXsbCQbYQri1YBzYgkXVhJcfTqV9M133LmmKQuvkKOj43tVpdg88i/mm19JutT\nap0A+xTzQJwTq1+5lPaW2V6y99sfP57O58//1x+8IEn6ye96h6ROL1DnZec5QHz98z95RZL0oQeP\nZR+leGFv53r8Dod9qdRrSLFe65bgGn/yhQuSpCdz2/De37yWfEDcvzvHAnnpiTPqq8BTv7Le9VHv\n81L3/OJ5dyTHO3PMMwr/n19OfW3HrOUqc96twVeXcmzm/uHaM+eMk70FH/xpjtWLa+m6Rw6n88zj\nA9Vewi6EvucTX0k++3ffmdJ1U59hfTOvubw/HV1MNrHP3Z/rRyxmTQI1CSTpA4+kfQa9wiMnk6+u\n53Es5bXEmuP5+NXM/z91hHoGeb1kPyyudHs7zz/8/sxzKb7RjZTnQ14Pv/inae//e9/7rtRHfqYt\n5mcce0lTD8PUv2A94wPsKrqebC/7L89xqxMgxiXpPacPSep4/PD1n19Mfn8y7x1fvZDOz/ZSWydy\n/Bw0tmBrXZ8EPz6er+GZ+eZyjquNLj7uVqT38HvoTXwPMZWX/BDCQUkhxnhNkmKMvz6Ndh0Oh8Ph\ncDgcDsfN45Zf8kMIf0fSFUnPKpXa3RdCuCxpWdLRGOM/HXNP4eSfcU6+w+FwOBwOh2OaCJ5CE9wO\nJ/9zSjz8h5Ry469Ien2nG2pO/omTJ2+ja4fD4XA4HA6HwzEJAa7d1BoM4ZskvSvG+P/sdN0TH3wq\nfuz3PlU46/A8D5lc0PBW4aVLHTcd7u0DmYcNn45cwnAU4Y4eO0BefXiQqb3Pvpx4/9/5zu4XD9qC\n+wlnF07kyWz3y5lDCW+N3x7hHuPdZ15MudS/8+1dH3B135lzisM9hkPc5TNO44WXyXnGD7+ZfMc1\n/vjFq7mPA02bHQd/rRnfw5k7it7hDdM3uZTr3NbwdmkLP8PRx2f4hDnFFvIyw5uE90g7UsdVhRv5\n6RcSp/LMkWQv3Eq4hfAe0SfA0X3mpXzfwX2NX6SO80yubvKawzslT3PH30xt//mFlI/533msFZPD\nf6y1FHC7yQNuazjgZ76EgLeML9CIwMGHSrlV5YAndokHdBbkz18vua3TOMm9z/zwmflgvDMVdxq/\nsrZo+9WsM0HTQV5z8jTbXN3oBOBUf+n1pdIHXFZ89qmvpTUEHxgbiCfWA/EItxQetK0DkPpPc279\nCde1rP+8YFg3XEcf8GvJx00MS10tB/KtM5dwp49lTjU1Epgf5u+17FNyxsP9ZZ6kbi4Pl/haa8bF\nHgF/3NaA2F/qeSS7P/vKFUnSd73rvtLHWvbFltECUcuEvZz1g1YKrvcXM4cdnQwcbDQiUse5JxbZ\nl4ibbfO8Yj1wHXEHzx9b61oVTCLxhS/QWRHn7IE8L+AxE7vB1Dlp9t/Q5nwnHrDz/hwnaDfmzb7M\nca4nDms9CXPHHLMF2JohxCqxjqaAGCfH/aXlNs++1PmdNtdLvOC7YXMdWijsRGtD/YhnXk5r+KkH\nj5Y+WA9Wl8d8fPL5RUnSdxUdQLoOrRc6jCt5H3gwPwuIL6nj8a8ZTRZzyT5b11OQRvdA1ih70vlK\nW4APuIY+eH7wbMU35PtnPbP3M+dct1rVRmCdf+6FtD7RW/DOwz5LLP78H70kSfr+dz7Q+OzLr+d6\nDfnzgaomDe807Ev78zpBy/G2XEvnkePzfxRjPKu7EO994oPxFz/6iT3t46nHDt+1468x9WJYMcbP\nS/r8tNt1OBwOh8PhcDgcu8Ntv+SHEP6KpMeV0mf+hlIKzfdJ+kyM8Znbbd/hcDgcDofD4dg1nJMv\naXp58l9UEtx+pxJD5Zqkq/aiEMLTIYTPhBA+c2nxwpS6djgcDofD4XA4HDWmQdf5ilI2nZ+vjv3+\nuAtjjOcknZMSJ38KfTscDofD4XA4HBnB8+Rn7IXw9odijL92o+s++OTZ+HuffKaISxARIRKiEAkC\nJIppSJ2IEoEL4jdbFAqBURHL5cYR0dAnormHT3QFtxDRIDYjXmZznytGoLNsxFkIUxHoFGFxJc7C\n8whtENV0n1NfiMbseLCBUEbwVk/pl89TMKQVFloBLUU9KNqCcA/xFoWsEAfVAiXaQox1tYgtW2Ew\nouoLWYiEuA6RHOIhBK91gQ4riMLP+BUhG0Pnzo1sNz5F3GWFxlIncCpFovJxBNIIwrCXvrEToe1K\nKfA0k8fViZQRk13P8XL8QCsGZe4Qgh3O4jjsPW/mhT7rAmjECeuDWLbiPfyNTcTCa1lIiKCd+Ziv\nRH+IERHrIVAjTriH+UB8RhziuzK+PPC6kBf+Y2wIVWkDESA+Y/3jq6HZ2tjrapEy8YCQnnuLeD/H\nND7qGUE3YP2zBogZqRPH4TPafC4X82HNWcEk84WwmHGHMbnh6oI5tT3EF4I89k7mEnEg57EBEfp9\nVYFA9lWKq7EOsCeaOZwriQGSLayfDTNvtZiUobHnMVdWRI5gkj2P+UFEi4dYZ4hMa3sQPDJmhNvY\nhS3EOOMp68c8G8atc/ZwfIdQlTY4vpjnjz7qglRSFwtrlQiTNtivEJ4SX2yfjId9GqEwRdbYHw5m\nH9WxZENtwQjoOc96R0zKfkDfrDjE4vW+e2kZIW0WzOcYpXAbPrlmnvvEBH2zh8ybWJe6dTsw69cK\niXkXmC+FNdP9iLSZ80F/dA1umvXBuwJj5172drsvM35arosoAp4t3HPAFPZEdMz+SozY5wz7E/O0\nWe1nrBmbJIHnA+M5eXDmrhWevveJJ+M/+5d7K7x98tFDd+34a0yLrlPjVAjhvw4hfMsetO1wOBwO\nh8PhcExECHv7/72CvXjJ56uGz9kTNSd/0Tn5DofD4XA4HA7HnmAvUmj+3zucK5z8Dz551jn5DofD\n4XA4HI6pIciT64Cpc/J3Czj58O2GET5bW0iC4/3e6JTB24RLyU/4ZXyGlw2ZbM7w7ShSA39e6rie\nwBZlgdMHrxOO3JzhYGID99d/5oFLuG4KdFj7LLcSzp8taGOLudS+4B7swM8As+Auwh+eHbS2WM51\n7RPLN7UcvlLQZjiea4kvx3ER7b30jxn0xb2TrsNXjGeu8hV2Ds1cTzo+nMBBBvi8XmKWy8kcMsfY\na3UmHIfPiY+Z83p90B/2WX68Le5TeMwTfMm81Euw4672m3HRVomJ7G9bQGl9q42joeHTJjuy5sT8\nbRQOteXFWy1HmafcN/vAoBoIPGrL57c8c+KFO+H/9kPb15aJkbpN2mIe4OxumDVq77OfWZPjdDE9\n46uyfs1+CkqMG+70OBtmTXwzP+wl9Awf2BYGtHto0QdUc844lsyewJwy96w5fArf3+qvGHddUIh1\nOjD+73zR7qtbZr/q+Og53vI4a52WLcTIWuub2CxFjAatLeVZZvaHei2w34byuY2PWaN1spqvaJ65\n7O11iOBH1vum0WZtmfVOfNnYHZjn5L5Kh8E9rEXWp91TZsxnePNWh1Ge0dujGomtMnY1bVpgC/PG\nVbQ5a+IytdmO2WqGmLqBiR80IvuMTmPGrF2p8wnzgf4CPcaRvNaYJ/Zf9BZrRttmtRXS5Gdpmets\nw9F9g7uWk/5NTzwZf+nX95aT/4FHvsE4+SGEp/PP09Nq0+FwOBwOh8PhuCmEPf7/HsE06TqnQggf\nkXQ8hLAdY/xFe0H+ReBpSXrozMNT7NrhcDgcDofD4XCAaQpvvyhpXako1tFxF8QYz8UYz8YYz544\ncXKKXTscDofD4XA4HGTK37v/7hVM7Zv8GOOv3Mz1IbR8OMuf7a4bdSa8RXiYcMYs/7zw/A0fz3LO\n7s+5oGtKneV0c85y8bcNj77LTd72ZfNSSx3/FT9sh9YXcbu1oeYYJptaDibt1FzdWcMjtdgybfYj\nfMg2B3aILVe55g9zbDa0vzNiL7mVGa+tjUAOYssNrfOZ21zPlivdcVxNvYXtNib6RQex3dwndX+B\now/rG0KRn8N8mlz1VnvAXMfK9diL/TOGt4/P8El3X2z6wlfw6+dmWg2JNMp33zA5oS1/3Npt+d01\nB77kVTc87a3t5Fco0PBMhzkmS671Xhtn+LrOAz4wfHHMYc3NmDzUJQ9+0Y30mvtsXndJmjF807Im\n87BIfW65+FanUNZFvq43Zr0RTjMm5z6xuWX3vvwTH8U4bGxtfF+46GraYk/cjuPndLjd+mzexFE9\njMJRh5ds5p44O3EQXnDLleb8TLaB3PXj0tGxt9v1QozOTlijFlavlPrL++2wjcUSX7nPebNvbRru\nftFp5BtrnnmpDZJzuXNN0UaYvq0WZNtoKOy81fcSH4N+y7W3dTCGhovPulrfbHna9V6Cf7taAoPG\nPrsfb5nc9lY3w95fR7rVIdg9cW4Qmuusz+xn9Bf1fNhaNFvludAb+/mAOd7pL9pnbF07xM6QXUvw\n4TeMHumQqYXCGrYaHmn0eQeO5xz7dj0crOpD1PdZ/c96NU/oEbtnfTo+O2hj1HFvYOrZdUIIp2OM\nr067XYfD4XA4HA6H40bw30USbuslP4TwX0k6LGlT0qKkVyX1Qgg/JumnY4znzfWFk3/GOfkOh8Ph\ncDgcDsee4HY5+V+R9KzSX5Sel3Ra6a9Wr6krilXQcPJPOiff4XA4HA6HwzFdeHKdhNv6Jj/G+HFz\n6Pdvpz2Hw+FwOBwOh+OWca+9ie8hps7Jvxn0e6ERfEidMLJXRHdZ7NHr/ugwa8Q+tpgJ4pQiOIqt\nCBYxFMVLECLWor/5QSua6USXvdxWWwhmWAR87XiwlevHFS2yharwCb6w47NiX9qxBTzqc7Y4yaYp\nEMb4tkxhJFTkVrxYCwutL4qgNvuVvuZMMRYrmLI218Il5iZst+JDfId9C0bgHDVs+8wirnEFnqwI\nq/OzLSKj5t4izCtz34qdx4kDi9DTCKlm+u291je2YA8Cy9lKNW59wz0UTsEe1sWsKYxWCsCYglC1\n4Ip/cQ47i/+zOYgv6buzsfUxNtdxRTxT9MaKF5c32oJctmCSLRaHmLkWA9pibxbW33P99vpOoK/G\nhlpgb33FOKzAHDHfwfnx2zJtj9PQ27EumMI6c0Wo2s4XVg5MIoFxwuFuvbYiRFtgi6HbvY8WbXEj\nbBo3VtAVDGvFmfMz7DGtMJfrixi+il2axk7sz7rmsv/iM0TLtjiQLfhWc4Dnzd7M3PfMnmALnA3N\nPNr47FUyzK6woRp77JyPxKopXmj3Gooz1W0QT3YP7IoLtoJ0u7/hc1sgKvmK/UYNWA/YZ4vc0cSw\nrKv0eWFMEoJONG78XQTO6bpNGzem756JhTpMbQzSNnbR5kwRSrd+LyJ+02f9XrKeBd1FwFwc2YqV\nbWIQu5x5F7L7ttT5fdYUjeveM0ZIGo67GFN7yQ8h/KikRyS9KOkBST8VY9yYVvsOh8PhcDgcDseN\ncC+ludxLTDNP/iFJlyXNSVrKnxuEEJ4OIXwmhPCZxcULU+za4XA4HA6Hw+FwgGnmyf/pXVxzTtI5\nSXryqbMT/kDucDgcDofD4XDcPII8hSaYJl3n6fwSrxDCo5JeilRvmYBh7Ph4FIoYZubacLvl+G1W\nPNqOe0+Bi5Z72HFA2z9UFI4lnMrQct7qgim2OIblJ0dz3bhiJXVfpZ3qHEWSKJyyf67l0dqCFxQe\n2c6+sm0zvs2K4zowPEbGsb6VeJcUDLE8Wctz7HzV8lWljrs3woulaM6EwiK2j65ISPpcz7n1r+Vn\n2qI5hWucz9viLF1RkG4cdi63ipZjO/umjSerx8BCOK1oPWoOONzHwucdw0eWRmMSm0rht+22uEvN\n3S8Fm4p+pC0Iht2b220cEV8U2LLc/LXNjqtLARfLmx3kazez/dgyMDzgbn7UfK5DntgsRbuIxR58\nbPj/sekbVxDLg16OcbjTXRdlbNi7aQo9dYWG2rnu+LOmgNtWy/mVKj524eK33OhZtBNz7fx08Zja\ngQlrYz711+5PNs6JgZlBywcmjoqGgL5Yy9W62zAcbkAbtsidLbJW9hjz9+N6adt91vLIZ806HpR1\n0c4bTY4rmrVteNgMx+oZQF/t2huM7ANtu/WY7b60vtHun1tmnMTofNmXxxdfrPsA1mdWTwK4C7/b\nPX9fxWlnzijMaItfdZqCtshU0fuYgmlFc9cfjSueE7ZInNUMWO0ZW+iIpqraW9FVWf1EKT5m5p73\nkaInG4zf++u4Khot8wxizJZHXzju7KGmQFVvyP7Q9W2f5yWWS2y2671oG83rCfM4P0Z702ke02d8\nZjVPjnsD0xTengohfETSE5KuSvpNpbSaDofD4XA4HA7HWwL/VSRhmpz8L0pal7QsaSb/38A5+Q6H\nw+FwOBwOx95jmpz8X8n//J0drnFOvsPhcDgcDodj7+Bf5Uu6g3nyt4dR19e2Ss76GcN5gyo2Lvc7\nPDj4gpYTCvcV3hq84RmTK3pguGU1x3XT5DGmTZu/meMlz7zhyNmc/jUBlX9h90rm5pccvIbHDHcP\n0KTNw15z5iy/HzvIJbxq2kTHgJnX17aaccBNXK1y5dIWnFY0EvAHr61uSuo46oXnb/mAJgfzdsUl\nnTecysITzLxzxkE+dsZJH9REmDNc69p31p4uR3WrHYDvCG8W/jzztc9wepv88iZHOnbavPKFh51P\nbKvVqECb5fqa1zxnYm695Bhv7bX8U7Qe+LrkVI/D5nhtP3NcckHHdm0N1PJ9u5zL7dq1/O0aHHv1\n0qok6eET+9oLKPmQfbiU483qH1ib9fogZosuwdQpsLUOsP/qSupjzsQ646zXKr6YMbm56bNoUgyf\nuQwvtn4ompYqdm2dC8D+BG/cjgO9CTUELE/d1mmQurU3kzVE+JO46nKpt3sg62bdcJLrNQh32tZG\nAOhFRutLwEVuc3xbP0jVPjXb8sgZqa1nsGH2V1NCRYtLG/n67hi1DrBvzehiSt71cUUPVNXoMDqy\npv6CydNP292jpuW245vtrTbuaJK+VqrYhQ/OXrzFs5Rx5EaYF/aI9SFaAjU2Fc1LqObHhJjVFIzU\nXcjjLbnvTYzyudbY2ToWtn7BYGSdt/uXtX9cH7aOB1atmdz23LFZNGrY2MbyttknartYSzbOwbx5\nBlm925p5J6rjytbjsHo4/3b23sKeveSHEE5LmosxOi/f4XA4HA6Hw/GWwPPkJ0yTk29xVtJ31gdq\nTv6li4t72LXD4XA4HA6Hw/GNi718yV/O/xfEGM/FGM/GGM8eO35iD7t2OBwOh8PhcHwjIoS9/f9e\nwZ7RdWKMH9/pfL8XtG+23/GFTS5lOJnwBGuOKxxJeI8j/Dqb93eOfNrpOubH5m/uNxy+zIs3nHvs\nLXlpBy1/ruT1F5xrNffX/NMZw0+G31u4iIbbV3I+m+Pbhh/Y8lgtv7fVGBQbzHnshrNneXl1zviN\nEX525rJmX8GT7/iQmV+qlvsHTxgOf50feHMkv33L17T860wJLRxlePJdzvh0vuY72lz02M+8WI6k\nzX8+UodhTF7hwls2udJnin9bjv6M4dlaDnnRb1Tzs2nymWMX89K3sZg/429igDizdSiqW7S80XJx\nqfkwY3LyF22LTHyZeaxjt+PtJzsfPDovqdOJUFfCctsPGD50x01Wc70krWf7D+a8/zb/95bx5Zrh\nsG8ajvvWsNVn1PdG49e1zVYPw/qxPFurq+G+uv7CgaJ3sTqM0LRh/4I9Z/QX3Mf4ahsPzLd6Fzu+\nonUi7mZazrvN/V5yfs91/GH8vmJ4zOwxRWMwaPeOmAcWDJ+5cJgrX4GL1xOX/viB2WSH8TPj4Sfx\nxr7MPB7ZN9PYKFX1SDZb7c38zKDxSakpYvY31uDSGjVU0n1zM5M54IQ1Y14yWqitYTsenp9Wc1Dn\nhN80c7oxRA+XzsO572qltHn90W6x/5bc9tUa5BmzlNc1dtmaCdhta+egCVs3ue7RCaQ20s+SXz4f\nx74DpqZJ0dLl64izYU44z/Gt5vmBT8bHoNXlsWfMDjjcxoLVEdTj6Jm4wYf4pLwbmPoGpcaIrcNQ\nzTm9lXgZmmfOBB2A4+7E1GYrhPDhcf92OBwOh8PhcDjeKoQ9/v9ewTR/JXt3COFzIYTvkPTUuAua\nPPkXPE++w+FwOBwOh8OxF5jmS/4bkn5tpwtqTv6Jkyen2LXD4XA4HA6HwyH/Kj9jmsWwfrX6+Ilp\ntetwOBwOh8PhcDhuDlN5yQ8h/MeSvhZj/IPd3rM9jFpa2yrCD8RP20WUlotObLdCGGlUGFUK8Qzb\n81YBjXi3iG+MgKouYoQI5vJyEmchzOvn47NZjHg5F8U5tj+dR6hEcSZMQITWFroZNucQFGIHAlQE\nRfcfTsJDtFcXrq1Lkg5l28Cb+bgk7afgixGDWlHo5eXU16EserKFRxBYdgKrbhyl+JUpwsTxzSJg\na0VBCEA3YyuoYu4RQ9X3LGcxFUKh69lHjMf6woqfOiFi63upmzN0TrbA2fxMK+DeKsXYWpEj9s+Y\ngmjJN7nt3C3XAnzGfKwj5jLCKisarwXWRRSbxYu2+BhXLq0mXyJUQ4Q2Z0R0nI/DztY1BOhGWE7f\nXRG5VqSJ2Ix5RDRnxV11/4x5Ps/tQmjHjn9Zi/uLqCw29rP+6yJ4M0YYjBiTIn1WTA1scSLaxg/1\nnGM/57iXglpnjqfiXst5/SNAHJh96lo+bvfKGiUmEe/NIULOtlBwK7SCyqt5HSEipXDS/koUi5AR\nHyHoJLEBMcq4iuA593F9vRVW2viUujWGoBxRKNfOFxF5up79ifuw7b5Dc40fsKk+Z/3L+JbW0nWH\ncwwgOGZ++hQ3y05l32btSt1eQh/4huNWqGrF7etGlF0K21X77pVlhLUUJUttIKoc9Nv4IJaX15KP\nbPFIngW1oNsW62P/mp9tx17s3mqTYpTYz7FycWm98YfU+agTt7bvAFaAOtPLyQaMz4qI3zzr6rbs\nuwHPWjxVnnc5FnieYO9A7TOuThTA85p957DZQ7oiisxDXufZ7is5RosQnLVZxe5+IxAmNnlP4fzl\nvA7w2X0I6Ulgwj7VH93femZdMELW2IT6bXcV0pft94ChbwGm9U3+CUkPhxC+TdJnJR2T9LsxxqUp\nte9wOBwOh8PhcDh2iWlx8k8o/TL8bG7ziXEv+LXw9uKiFzDxyAQAACAASURBVMNyOBwOh8PhcEwR\nwfPkg6l8kx9j/Dv8O4SwT9L8hOvOSTonSe//4FOjf2t2OBwOh8PhcDhuA/fQe/ie4pZf8uHhSzpQ\nF76KMa5I+q0b3d/rBR2YH3T808wNu3R5LX9ueY9NgZj86wHcvdXM1zyyv+Wqwk+Fo3f+amob3tr9\nhxM3cza3t1wVz6C/Uqgj8+0OU/iEohnZzkuZA8dvLvDoThxMfcD3PFhxxuH1vXxxRZL00LGFPC41\n46Fvrn81+wgb4a/CO7xQcfKPnTqQxpHbXMycSDiuJw6mPuBWWi4rgApKYSs4gJItvtXxNuHHFlvy\neODZFl7tBr5M44DLWDf7zHOXJEnf8tjRZM+w5UTii5XMOaztk6STeR4Kdzy03Py6zX5ua2Nruxl7\n4RavtVz2og9Ya/UCcDO3hx2/lnsuXMvc730UYUrnr2WevC22tG74qRulmFbLrU7XoE9IbbDG4Hzi\nm5OH2tikzaPZJviotjiLJF1cSm3sK5ztttgN/FRi4CixbHjlrDl4q3UBG+zhWtYBfVzJ47gva1Xg\nk3Mc+wunP9/3woWuEDdrrvRJATQTT1uG398VsDNFZUyBO6kuhIf2JLQ+yeOyhXhsoZtBbtLub5J0\ndWuzsZu9A30O/N6DOSbgHjNP3TjadcV8Sd36ZA8o+qL8Az+zR8K/JpYJH2Ki82XpoviRcbx+Za1p\nk0t5Xqxs5JjI9h+YG6+/4P7aJ+yb8MdL4a1SBK/TI0ijxQithqrer4pubELROvQvfGbdD4ueqY2z\ndaPdkbp4Zw3x3Hj50mpjF2sUe/E3vuZ5yLOhLna0MNv6YKbfrgvOrxefpbbqZ5DUPfdYk/Wzlv22\n0yO0MYDWhnmqOepSN1/4YyuOcvLROBw0Rd5swTZb6Ilx7g/t564YZBe8+OKQ4bmX5wIFwtAG9tsY\nOWKeBaN9dfHBfo8fe2a9ss7REOIj1gPrG/79aqUB6Q9b3d620R3Nm5hw3N24nW/yT0g6KOlqCOF/\nkfQnkh6U9IKk05J+IcZ49bYtdDgcDofD4XA4dgv/Kl/S7b/kr0i6X6lK9MX8/1b+/IikP61vCCE8\nLelpSTpz5uHb6NrhcDgcDofD4XBMwi2/5Nc8/Ju4p3DyP/jUWefkOxwOh8PhcDimiHDHU2iGEL5X\n0k8pcVn/SYzxfzXnQz7//UpfmP+1GONnd7o3hPD3Jf37kjYkPSfpr8cYr+xkx9SKYd0sglqOL9zL\noznfPLzNwZg8rnDZ4DHChSw8v9hex/mNksM3fYbbB0+wV5Eq4bdb3ibH4b6VPNmbbf5jQJ/wius8\n+VEt983m/f7am8vZB4bfmc/DoYRvuLLe8g4l6bXM3ycPM3585MS+xu7H79svSXo18znpEy77jMkv\nf6TiuNraAPQBl3Dd5Am3vrE5r0GdBvyRnEucOSMu+iXHcptr+dSRNMlfu7DS9LFi8lfXvFvGjP3w\neOHwlhz3uS3mAV4zOod+jhF4kXUu/DKm3AjcVfQKzCWxzDgX8vyxLh7IPHTWDfMpdX6Fc0s+/GM5\nBuFI0xYcdmKAmH/tcoqFw3l9XDI6B0l682qyH17v9bXNPM5ceyIfJ37mBm2+ZrjU2Ly/qo2wRZ2I\nzba+BXPIGuz31pvxzZpc6vBPiR10J1I3N29kvY7lslOroeNUp/vWC3e9zSUNxnGoiROo9N0agl+L\ntqj1Ue0TqdMUPHpyfzlWa0tS/23NB/jZXV2Atm3ux8eMv96j1wL6kHQv62Ngam3A8z2e9w58S4xf\nWm5z8g8qzvGLi2m9nslaCeyDW2z58BeM5uCA4ZcfL/tXN44H8t7AmLGbOeZ5YDUFwO5jr+Z1Us9H\nNHshccY+zF5dcr9nH8722r399NHkh8W8VutaLuzd+PPBo2lczBn589nj4Iw//0b7XOkZ3nadJ5+5\nwR7mkLoK7LOsLdqiz9fy84T9ij1k3OsX65TnFxo6nkEXsg2sX543ZZ9ba/VMnJe6fRKglSuaoBIg\nbb0M2rLrh3msdTHP5+c1+/7AXMt8Mec8T2ib8zwjmMda24Z/0RLZmhoD825ka2ps5p/L2TfU96n3\nK/yPFgKN1rXKn46dEULoS/ppSd8j6RVJnw4hfDTG+IXqsu+T9I78/4ck/WNJH7rBvb8j6W/FGLdC\nCP+bpL8l6b/fyZZppdBsEEL48F6063A4HA6Hw+Fw7IQ7nELzWyV9Ncb4fIxxQ9IvS/pBc80PSvr5\nmPAHko6EEE7tdG+M8f+NMaJa/wNJD93IkKm95IcQfjyE8LdDCN8h6akJ15Q8+YsXLkyra4fD4XA4\nHA6H427AaUkvV59fycd2c81u7pWk/0zSb97IkGl+k39M0ld2uiDGeC7GeDbGePbEyZNT7NrhcDgc\nDofD8Y2O8Bb8L+kEX1rn/59+a0YnhRD+tlKSm392o2unxsmPMf7d6uMnbnx94lfOFh50+kkub/hq\nq4WX2nER4TPCY4TLCn/z9czlg7e2arhk8NUezjxmOJU1yHsPzxIuXJe3fLtpe1/h7iY7788cRPIc\nw/3brDh8cIvp66DhfpLHnzvIUw1vFR7dux9caMYFf7K2H14s3FVypcMxhP8LL/7UkY7jXbcN77nO\nVQynEI46Y4XPOGO47mBQ8m7n+zK/cDv7rB4HHNy335/y/tvcyXA9S670lezvPB54q+TRttoJqfMn\nfHI41MTbgfmsIcjXf/n8dUldnB0/mNpmPpcLZ7z72x68WH7CFSUWyF0PLxYu7wtZW0CNAVx5f76+\njuEzWb/Q5c9OFzPncFqXTb5/1h46hrdljj72ww+WOi4ofcyauYery/pg5pknuL3Fx4z/YMeXhytN\nPnnqSRwscZX6JCaxH10J44SXzR5Sxy5jY+/4wusp6+/bTx5UDeaU64pmIt+PDfBtX7nUzcfb7k9+\nvHIx9cv8FM1DjlnaoC/WDXsIe+LJSlMAGDv3Ppjnqme4uvTJPoRPaBsuPlze16q4wj7mwbbB+rex\nUPLmZ9u4Dg4zHHmp4+IT5+wZjI/9t9PHtHuL1d6E3Ge93pfW0hgfPNLWbDyf1yK+O1ZqOeQ9Pq8T\n5razO8dfpVlBO4Ae5szx1Oa6qYWA71gn+P9U5tezZq+Z/U7q9C1/fj4Vl7faAfrCN+Sq35+vI5aJ\nL9ZLvT7mTA0QYpdNkD7gpj94NLXB3KJzgE9fcsRXeyJ1IPAv+z5zix7B1kZhDyRWF3PtDjjwtdZj\nNdc8oS4JeyI+o+0L19PYH83vBm/k+WC/Yx9DF1PH7tsfONDYfyLbxT1oQFgHxDC+pS3WF/tercPo\narC0NSlWN9p3omhqBdAmez06K2KZvmoU7V/Z+9qaCA4txhjPTjj3qqQz1eeH8rHdXDOz070hhL8m\n6SOSvjvWBVkm4Ja/yQ8hfNhy70MIp/PPx2+1XYfD4XA4HA6H45bxFnyVvwM+LekdIYTHQgizkv6S\npI+aaz4q6T8NCd8m6WqM8fWd7s1Zd/6mpP8gF569IW7nm/zHJc2EEC7nfz8q6c0QwsOSXgghLMcY\n36hvqPPkP+R58h0Oh8PhcDgcX0fI2W9+QtJvK6XB/NkY4+dDCD+Wz/+MpI8ppc/8qlIKzb++0725\n6f9T0pyk38l/0fqDGOOP7WTL7bzkPy/plKTvUPot43L+/JJS1duR2sdNnvwnPU++w+FwOBwOh2O6\nuNN58mOMH1N6ka+P/Uz17yjpx3d7bz7+9pu143aKYX3cHPrqrbblcDgcDofD4XA4poewC97+5Jsz\nJ79+4Q8hnI4xWoHBCD7w5FPxd3//mVJMqlfENOnzShGStOI6qRORIHY9nItPIFZEGIJwZD6LYS9U\nQs5030xzfS3OQjxjhZKDXvvboS0AhSgIvyL6Q2BYF0bCPsaMqGrdFOJC+EWbCN5oE6EYn2sgTkKQ\ng4/wN8XHrq+3AmfGg+iHojP4fGF25A81RbyLaK4U98nioCIKytf3zZzP9FuJyGZV4Ofli0kg9FAW\nsDGn2Mm4EDF1BUdiYwOwPq3vwf+Hclx1QsLtpi98dSELvigaQhE2xlMLvxEGEke2bYRgiGLxAXHF\n/deNaLYWy7FWmDv6L8XeTBtXssD1naeScAyhMZ4h5OsiOdjJvCAUHBRhd2qzX4qOtcXYrPCZtVCL\ns/EJosvTuQ/mZ83MwzZF2bIovhQ1ysIxxMCb1ThWS/GxfE8eFzGJNdzDuAmbDSMEtUJwqZsH1hgF\naAYm3tnXEHJjE6JZjCFOa/Ei/94wYkvWXNnjiMnNNhkB9iMwJJa3qvlgPXAt+82MKUxnbeIn88Ce\nszymeB9tWj9jB/syiQ2K2DXHNkXjbAGxugghvqAQ26optobvuIXYpAUr8uW6ei9hj6ZNrrXPOZ5/\njJf7Sozm+9grl6u9BL/ZQpKsD56hCFNtoST6OH4wnWePf7AS2HMNfTyWxfjsN/jCzr0VFJciUnkt\n1MXbEO3auMYexkVsItrfNM8ZvI9flqrnIQJVWwyRfXXDvAOsZNE4cTc/0z7vuiKM3THuZTy2eOVK\n2YfT9ezd0axrK06+PKYIISJp4oV5sMkFWAeMj1jnOOPcX61B7MdH62afBQfn+3+0g/D0juKJDzwV\nf/33PrWnfTxyfP6uHX+N282uM4mXvx1j/MXbNc7hcDgcDofD4XDcPG43T/7zkq4p8fI/l/9flnR0\n3MV1MayLi4u32bXD4XA4HA6Hw9HizibXuXtwW9/k3ywvvxbefuDJp1x463A4HA6Hw+Fw7AFui5N/\nO3jyqbPx9//tpws/jSIucL45DufsQMMZywU44ANO4CtbXiecODh73F9zvwFtwmMcmKJRHIc3B9c+\nGn4518Gzq7l92ANnD04ePE3sYzzwymlzy/A+4c7V/FM4uIyR/lfMOOx1tAWvmeJfFPuq+dlwEZlD\nipHQF9bgG3w1MNx1ztN3XQQEziFjpigIv1FjD21CJcbvcMItJ7bWAcA/nTF88SuF39jqMvqG1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x268708d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XgJmdMbP7zez+C+fO7Y/VQgghhBBiPLB9+Bsxhn2B8BkAb/HXOQAbAJ4TQvh5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\nCCGEEEIIIQp0gyCEEEIIIYQo+H9OPFhGnqKEHgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26a2a0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "input_tensors = [model.input, K.learning_phase()]\n",
    "saliency_input = model.layers[2].output # before split into branches\n",
    "saliency_output = model.layers[11].output # class score\n",
    "gradients = model.optimizer.get_gradients(saliency_output,saliency_input)\n",
    "compute_gradients = K.function(inputs=input_tensors,outputs=gradients)\n",
    "\n",
    "for idx,doc in enumerate(x_test_my_idxs):\n",
    "    matrix = compute_gradients([np.array([doc]),0])[0][0,:,:]\n",
    "    tokens = [index_to_word[elt] for elt in doc if elt!=0]\n",
    "    to_plot = np.absolute(matrix[:len(tokens),:])\n",
    "    fig, ax = plt.subplots()\n",
    "    heatmap = ax.imshow(to_plot, cmap=plt.cm.Blues, interpolation='nearest',aspect='auto')\n",
    "    ax.set_yticks(np.arange(len(tokens)))\n",
    "    ax.set_yticklabels(tokens)\n",
    "    ax.tick_params(axis='y', which='major', labelsize=32*10/len(tokens))\n",
    "    fig.colorbar(heatmap)\n",
    "    fig.set_size_inches(14,9)\n",
    "    fig.savefig(path_to_plot+'saliency_'+str(idx)+'.pdf',bbox_inches='tight')\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Thank you for your interest!\n",
    "If you found any error or have any suggestion for improvement, please file an issue on GitHub. You can also contact me at `antoine.tixier-1@colorado.edu`.\n",
    "If you use some of the code in this repository in your own work, please cite:\n",
    "* bibtex:\n",
    "```\n",
    "@article{tixier2018notes,\n",
    "  title={Notes on Deep Learning for NLP},\n",
    "  author={Tixier, Antoine J-P},\n",
    "  journal={arXiv preprint arXiv:1808.09772},\n",
    "  year={2018}\n",
    "}\n",
    "```\n",
    "* plain text:\n",
    "```\n",
    "Tixier, Antoine J-P. \"Notes on Deep Learning for NLP.\" arXiv preprint arXiv:1808.09772 (2018).\n",
    "```"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
